System and method for consent detection and validation

ABSTRACT

A system is provided for consent detection and validation. Uttered speech signals and sensor data of at least one user is received from a first electronic device during a personal interaction, scheduled based on a consent response corresponding to an acceptance of a consent request, between two users. A confidence score is determined based on intent of the two users, current sensor data and a new set of user characteristics for the two users during the personal interaction. An immediate consent or dissent of one of the two users is detected at defined timestamp during the personal interaction based on comparison of the confidence score with threshold value, explicit or implied keywords from uttered speech signals, and extent of deviated values of sensor data. Based on a plurality of criteria, immediate consent or dissent of one of the two users is validated and second set of tasks is performed.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

None.

FIELD OF TECHNOLOGY

Certain embodiments of the disclosure relate to data processing systems.More specifically, certain embodiments of the disclosure relate to asystem and method for consent detection and validation.

BACKGROUND

With the widespread expansion of the Internet and mobile infrastructure,in collaboration with the breakthrough technologies, such as datasciences, general communication networks today are being redefined ashyperconnected networks. An exemplary hyperconnected network essentiallyincludes IoT-connected endpoints, cloud computing, 5G and advancedconnectivity, mesh networking, edge computing, and artificialintelligence (AI). Backed by high-speed and low-latency communicationprotocols and programmable infrastructure, such hyperconnected networksmay be realized by smart electronic devices to facilitate diverseservices. Non-limiting examples of such services may include exchangingmessages (for example, via e-mails, texts, video chats, voice chats, andinstant messaging), watching videos, controlling various devicesremotely in real time through application programs, on-the-moveshopping, and easily accessing desired information from anywhere andanytime. Such hyper-connected networks may facilitate various networkingplatforms (such as, social networking sites, dating websites, andvarious application programs) that provide the users with an on-linemeeting and socializing place with other users for the purpose offorming business and personal relationships.

In certain scenarios, one party, such as a user who initiates thecommunication, decides whether to advance the relationship with theother party and meet in-person or otherwise communicate outside of suchplatforms (for example, via personal email, phone calls, text messages,and other electronic communication not routed through or otherwiseinvolving the social network or dating system). Once the relationshipprogresses beyond such in-person meetings and other interactions, thenetworking platforms typically have little or no control over theinteractions and do not record or track information about suchinteractions to draw a line from acceptable behavior all the way to acriminal behavior. Further, such network platforms may not be able torecord or facilitate any permission or a mutual agreement, also referredto as consent, in case the in-person meetings progress to physicalrelations or intimate interactions. In certain scenarios, such physicalrelations or intimate interactions may result in sexual abuse, sexualharassment, sexual misconduct, or other violations of consent. In suchscenarios, the victim may not come forward against the perpetrator andmay be easily threatened into silence due to lack of any legally soundevidence. In other scenarios, an individual, who may be a malicioustrickster, vindictive, a fame-seeker, a mentally unstable patient,and/or an easily influenced or delusional person, may level falseaccusations or allegations resulting in mental agony and an immediatesocial downfall of an accused. In such scenarios, the accused, despiteof being an innocent person, may not be able to defend himself/herselfdue to lack of any legally sound evidence again.

Thus, security systems, such as audio or audio-visual recorders, may beused to record the consent of both the parties, before or during suchin-person meetings, about the permissible extent or set boundaries ofphysical interactions. Such an arrangement may safeguard both theparties from any possible false allegations in future regarding theconsensual nature of the interactions. However, one or both the partiesmay not agree to the usage of such security systems due to privacyissues as such security systems may be easily tempered with catering tothe intent, need or convenience of one of the parties.

There also exists a provision of duly signing a written agreement or aconsent form (with a witness present) that relays in clear terms theintent of two consenting parties to participate in physical interactionstogether. The agreement or form allows the two parties to enter the dateand time the activity is to occur and list the exact permissions made bythe consenting party. The agreement or form further includes that incase things go beyond what was originally consented, the two partieswill have to mark that it will be ruled an accident without anyrepercussions or that the accident will be determined as assault.However, the written agreement or consent form, standing alone, are notperfect instruments. A signed consent form does not necessarily provideinsight into comprehension, state of mind, or capacity of the otherparty. For example, there may be no indication of trickery,disagreement, debility, attention lapses, coercion or other such aspectswhich may have prevented the other party from fully understanding thenature of the information conveyed, arguably rendering any such writtenagreement or consent form ineffective. If the consent is ineffective,the other party may suffer legal, physical, pecuniary, or other setbackwhich was not necessarily contemplated by the other party. The otherparty may attempt to hold the initiating party liable on variousgrounds, for example failure to effectively communicate the information.Therefore, despite the existence of a duly signed written agreement orconsent form, uncertainty over whether the information was effectivelycommunicated to and understood by the other party may expose theinitiating party to liability. Thus, any of the existing systems do notprovide a smart, secure, robust, legitimate, and user-friendly tool forconsent detection and validation.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present disclosureas set forth in the remainder of the present application with referenceto the drawings.

BRIEF SUMMARY OF THE DISCLOSURE

Systems and/or methods are provided for consent detection andvalidation, substantially as shown in and/or described in connectionwith at least one of the figures, as set forth more completely in theclaims.

These and other advantages, aspects, and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a network environment forconsent detection and validation, in accordance with an exemplaryembodiment of the disclosure.

FIG. 2A is a block diagram that illustrates an exemplary electronicdevice, in accordance with an exemplary embodiment of the presentdisclosure.

FIGS. 2B and 2C depict two views of a display unit of the exemplaryelectronic device, in accordance with an exemplary embodiment of thepresent disclosure.

FIGS. 3A and 3B depict block diagrams that collectively illustrate thevarious components and data processing engines of a consent managementsystem for consent detection and validation, in accordance with anexemplary embodiment of the present disclosure.

FIGS. 4A, 4B, and 4C depict flowcharts that collectively illustrateexemplary operations for consent detection and validation, in accordancewith various embodiments of the disclosure.

FIG. 5A illustrates a first sequence diagram for operational stepsperformed between a plurality of electronic devices and the consentmanagement system for consent response (acceptance or rejection), inaccordance with an exemplary embodiment of the disclosure.

FIG. 5B illustrates a second sequence diagram for operational stepsperformed between the plurality of electronic devices and the consentmanagement system for consent cancellation, in accordance with anexemplary embodiment of the disclosure.

FIG. 5C illustrates a third sequence diagram for operational stepsperformed between the plurality of electronic devices and the consentmanagement system for consent negotiation, in accordance with anexemplary embodiment of the disclosure.

FIG. 5D illustrates a fourth sequence diagram for operational stepsperformed between the plurality of electronic devices and the consentmanagement system for consent detection and validation, in accordancewith an exemplary embodiment of the disclosure.

FIG. 6 is a conceptual diagram illustrating an example of a hardwareimplementation for the exemplary electronic device of FIG. 2A employinga processing system, in accordance with an exemplary embodiment of thedisclosure.

FIG. 7 is a conceptual diagram illustrating an example of a hardwareimplementation for the exemplary consent management system of FIGS. 3Aand 3B employing a processing system for consent detection andvalidation, in accordance with an exemplary embodiment of thedisclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Certain embodiments of the disclosure may be found in a system and amethod for consent detection and validation. Consent is a term of commonspeech, with a specific definition as used in a variety of fields, suchas the law, medicine, research, and sexual relationships. Consent may bedefined as an agreement according to which an individual voluntarily andwillfully agrees to undertake an action that another individualsuggests. Recently, following numerous reports of high-profile untowardincidents and the development of various social movements, there isrequired an efficient, legit, and reliable solution that can function asan effective deterrent for growing issues of improper behavior betweentwo parties during a personal interaction. For example, the proposedsolution may provide a legally sound evidence that may be used by acomplainant to file charges of consent violation against an accused. Onthe other hand, the solution may provide a legally sound evidence thatmay be used by a defendant to assert the truth of the matter in casefalse charges are filed by an alleged complainant.

In accordance with various embodiments of the disclosure, a system isprovided for consent detection and validation. The system includes amemory for storing instructions and a processor for executing theinstructions to perform operations. The processor may be configured toreceive, during a personal interaction between a first user and a seconduser, uttered speech signals and sensor data of at least the first userfrom at least the first electronic device, via a secure communicationchannel. The personal interaction may be scheduled based on a consentresponse, received from the second electronic device, corresponding toan acceptance of a consent request received from the first electronicdevice. The processor may be further configured to determine aconfidence score based on an intent of both of the first user and thesecond user, current sensor data and a new set of user characteristicspredicted for the first user and the second user during the personalinteraction. The processor may be further configured to detect animmediate consent or an immediate dissent of one of the first user orthe second user at a defined timestamp during the personal interactionbased on at least one of a comparison of the confidence score with athreshold value, one or more explicit or implied keywords from theuttered speech signals, and an extent of deviated values of the sensordata. The processor may be further configured to validate the immediateconsent or the immediate dissent of one of the first user or the seconduser based on a plurality of criteria. The processor may be furtherconfigured to perform a second set of tasks based on the validation ofthe immediate dissent of one of the first user or the second user.

FIG. 1 is a block diagram that illustrates a network environment 100 forconsent detection and validation, in accordance with an exemplaryembodiment of the disclosure. The network environment 100 is shown toinclude edge resources 102, a plurality of electronic devices 104, cloudresources 108, a consent management system (CMS) 110, and differentcommunication networks, such as a local area network 118 a and a widearea network 118 b, as further discussed below.

The edge resources 102 may comprise suitable logic, circuitry, andinterfaces that may be configured to collect and process data for theCMS 110 and/or communicate with one another via the CMS 110. The edgeresources 102 may be considered part of the Internet-of-Things (IoT)having enough storage and computing capacity to make low latencydecisions and to process the sensor data in milliseconds. The edgeresources 102 may be interchangeably used with IoT devices that arelocated, deployed, and/or connected near a network edge that maycorrespond to a physical point at which the local area network 118 a,such as an enterprise-owned network, connects to the wide area network118 b, such as a third-party network. Non limiting examples of the edgeresources 102 may include personal electronic devices (such as theplurality of electronic devices 104), local servers (such as a databaseserver 102 a and an application server 102 b), and communication devices(such as edge gateways or routers 102 c). It should be noted that theabove instances of the edge resources 102 are merely for exemplarypurposes. Other examples of the edge resources 102, such as routingswitches, integrated access devices (IADs), multiplexers, a variety ofmetropolitan area network (MAN) and wide area network (WAN) accessdevices, and various assets, such as people, buildings, manufacturingfacilities, retail facilities, network, or communication infrastructure,and any associated IoT devices, though not shown in FIG. 1 for brevity,may be possible without any deviation from the scope of the disclosure.

In accordance with various embodiments, the edge resources 102 maycommunicate with each other and/or with other remote networks andresources (for example, the cloud resources 108) through one or more ofthe communication networks, such as the local area network 118 a and/orthe wide area network 118 b. In accordance with an embodiment, thefunctionality of the CMS 110 may be partially or fully implemented byvarious devices from the edge resources 102.

The plurality of electronic devices 104 may be configured tocommunicatively couple and interact with one another, other devices fromthe edge resources 102 and the cloud resources 108, via thecommunication networks, such as the local area network 118 a and thewide area network 118 b. Numerous examples of the plurality ofelectronic devices 104 may include, but are not limited to, asmartphone, a tablet personal computer (PC), a slate PC, a personaldigital assistant (PDA), an Ultrabook, a wearable electronic device(such as smart clothing, head-mounted display (HMD), or smart glasses),a smart television, a desktop computer, a laptop computer, and othersuch electronic devices and Internet Protocol (IP) appliances.

In accordance with an embodiment, the plurality of electronic devices104 may be configured as thin or ultra-thin clients enabling remotedesktop applications. In such embodiments, application software may beallowed to run on a centrally-hosted virtual computing system, such asthe CMS 110. Such thin or ultra-thin clients may rely on access to theCMS 110 each time input data needs to be processed or validated. Theplurality of electronic devices 104 may provide an infrastructure toenable the downloading of various application programs, such as anapplication program 112, and may facilitate browsing of various onlineplatforms, such as social networking sites and dating websites.

In accordance with an embodiment, each of the plurality of electronicdevices 104, such as a first electronic device 104 a and a secondelectronic device 104 b, may be configured to download the applicationprogram 112, referred to as an “app”, that facilitates a variety offunctionalities for the associated users. Examples of suchfunctionalities may include, but are not limited to, enabling a firstuser 106 a (associated with the first electronic device 104 a) to searchthe user profile of a second user 106 b (associated with the secondelectronic device 104 b), recommending the user profile of the seconduser 106 b to the first user 106 a, various modes of electroniccommunication between the first user 106 a and the second user 106 b(such as managing consent forms, exchanging text messages, facilitatingpersonal email, phone calls, video calls, and the like), facilitatingvarious tasks (such as recording, processing, analyzing, and publishingthe speech signals uttered by the first user 106 a and the second user106 b during a personal interaction), and rendering alerts in responseto detection of an unwarranted situation, such as an immediate dissentof the first user 106 a or the second user 106 b during the personalinteraction, and the like.

For certain devices, such as desktop and laptop computers, theapplication program 112 may correspond to desktop apps. For otherdevices, such as smartphones, the application program 112 may correspondto mobile apps. The mobile apps may be of three basic types, i.e.,native apps, web apps, and hybrid apps. The native apps may bestandalone apps that are downloaded and installed at the plurality ofelectronic devices 104. The native apps are built just for one specificplatform or operating system, such as Android® and iOS®. The web appsmay be accessed via a web browser and are responsive versions ofwebsites. The web apps may have limited functionalities due to anextensive dependence on the web browser used by the plurality ofelectronic devices 104. The hybrid apps are a combination of native andweb apps, i.e., web apps with a native app shell. The hybrid apps mayhave a home screen app icon, some responsive design and may even workoffline.

The plurality of users 106 may correspond to the personnel who operatethe plurality of electronic devices 104 for interacting andcommunicating with each other, via social networking platforms and otherapplication programs, such as the application program 112. Each of theplurality of users 106 may create a user profile on such socialnetworking platforms and other application programs, such as theapplication program 112, and search for the user profiles of the otherusers to interact with them. Each of the plurality of users 106 may beassociated with a corresponding electronic device from the plurality ofelectronic devices 104. For example, the first user 106 a from theplurality of users 106 may be associated with the first electronicdevice 104 a from the plurality of electronic devices 104. Similarly,the second user 106 b from the plurality of users 106 may be associatedwith the second electronic device 104 b from the plurality of electronicdevices 104.

In accordance with an embodiment, the first user 106 a at the firstelectronic device 104 a may search for the user profile of the seconduser 106 b based on one or more search parameters, such as name,location, name of the workplace, name of the educational institution(such as name of school, college, or university), and the like.Alternatively, the user profile of the second user 106 b may beautomatically recommended to the first user 106 a by the CMS 110 usingone or more methods. Accordingly, the first user 106 a at the firstelectronic device 104 a may select the user profile of the second user106 b for initiating a conversation via the application program 112.Once the conversation between the first user 106 a and the second user106 b is initiated, the relationship between the first user 106 a andthe second user 106 b evolves and proceeds to the next level, and thefirst user 106 a and the second user 106 b may decide to perform one ormore activities, for example sexual activities, during a personalinteraction. In such case, the first user 106 a may want to execute aconsent request so that the first user 106 a is safeguarded from anyfalse allegation by the second user 106 b in future. Thus, using theapplication program 112, the first user 106 a may generate a consentrequest at the first electronic device 104 a for transmission to thesecond user 106 b, via the communication networks (such as a local areanetwork 118 a and a wide area network 118 b). Thus, the first user 106 amay be hereinafter referred to an ‘initiator’. In response, the seconduser 106 b at the second electronic device 104 b may accept, reject, ornegotiate the consent request using the application program 112. Thus,the second user 106 b at the second electronic device 104 b may behereinafter referred to a ‘recipient’.

The cloud resources 108 may comprise various resources and/or servicesthat may be hosted remotely over a network, which may otherwise bereferred to as in the “cloud.” In accordance with an embodiment, thecloud resources 108 may be remotely hosted on servers in a datacenter(for example, remote application servers, such as a digital distributionplatform 114 and remote database servers comprising an installation filerepository 116). The resources, services, and/or functionalities of thecloud resources 108 may be utilized by or for the edge resources 102,via a combination of the local area network 118 a and the wide areanetwork 118 b. Non limiting examples of the cloud resources 108 mayinclude outsourced storage, processing power, databases, networking,analytics, artificial intelligence engines, navigation services,geolocation services, network or infrastructure management, paymentprocessing, audio and video streaming, messaging, social networking,news, and software applications.

In accordance with an embodiment, the cloud resources 108 may delivercloud computing for the CMS 110 over the wide area network 118 b. Thus,the cloud resources 108 may provide the CMS 110 with flexible resources,faster innovation, and economies of scale so that the consent detectionand consent validation are performed by the CMS 110 in the fastest andmost optimal and robust manner. In accordance with differentembodiments, different components of the CMS 110 performingcorresponding functionalities may be partially or fully implemented byvarious cloud resources 108 as an integrated or a distributed platform.

In accordance with an embodiment, various cloud-computing providers mayoffer different services according to different models, such asInfrastructure as a Service (IaaS), Platform as a Service (PaaS), andSoftware as a Service (SaaS). The IaaS form of cloud computing servicemay offer computational, storage and networking resources on-demand,usually on a pay-as-you-go basis. Examples of the IaaS may include, butare not limited to, Amazon Web Services (AWS) Elastic Compute Cloud(EC2)® and Microsoft Azure®. The PaaS form of cloud computing servicemay provide a framework for application creation and deployment. Forexample, the technical stack, such as, AWS Beanstalk® and Google AppEngine®, required for application development may be available on thecloud in the PaaS form, which requires no download or localinstallation. The SaaS form of cloud computing service may correspond toa web-based software deployment model that makes the software accessiblefrom any device through a web browser, irrespective of where thesoftware is hosted, which operating system it uses, or which language itis written in. Non-limiting examples of the SaaS may include, MicrosoftOffice 365®, ZoomInfo®, Dropbox®, and the like.

The CMS 110 may comprise suitable logic, circuitry, and interfaces thatmay be configured to perform consent detection and validation, inaccordance with an exemplary embodiment of the disclosure. The CMS 110may be powered by various innovative technologies to, for example,extract value out of the troves of data they collect, deliver insights,simulate human intelligence, generate intelligent recommendations,automate tasks, and provide advanced system capabilities. The CMS 110may include various components and data processing enginescommunicatively connected with each other, via the local area network118 a and/or the wide area network 118 b. In accordance with differentembodiments, various functionalities of the CMS 110 may be partially orfully implemented by the various edge resources 102 and cloud resources108. The CMS 110 may be implemented, based on a number of hardware andprocessor technologies, as further described in detail in FIGS. 3A and3B.

The application program 112 may comprise suitable logic and interfacesthat may correspond to an application software (or an app) dedicated forperforming various functionalities by providing a user interface that ispresented on the display screens of the plurality of electronic devices104. An installation link for the application program 112 may beprovided to the plurality of electronic devices 104 by one or morecontent providers over the local area network 118 a and/or the wide areanetwork 118 b. In accordance with an embodiment, the installation linkmay be part of an advertisement, or an ad campaign provided to thecontent providers by an Ad server. The advertisement may be displayed ona web page or an app running on the plurality of electronic devices 104.When the installation link for application program 112 is selected, theplurality of electronic devices 104 may be redirected to the digitaldistribution platform 114, for example, GOOGLE PLAY™, APPLE™ App Store,and others. The plurality of electronic devices 104 may retrieve ordownload the installation file of the application program 112 from theinstallation file repository 116, via the digital distribution platform114. Once downloaded, the application program 112 may be installed andexecuted on each of the plurality of electronic devices 104.

In accordance with an embodiment, the application program 112 mayfacilitate a variety of functionalities for the associated plurality ofusers 106. Examples of such functionalities may include, but are notlimited to, enabling the first user 106 a to search the user profile ofthe second user 106 b, recommending the user profile of the second user106 b to the first user 106 a, various modes of electronic communicationbetween the first user 106 a and the second user 106 b (such as managingconsent requests, exchanging text messages, facilitating personal email,phone calls, video calls, and the like), various tasks (such asrecording, processing, analyzing, and publishing the speech signalsuttered by the first user 106 a and the second user 106 b during apersonal interaction), rendering alerts in response to detection of animmediate dissent of the first user 106 a or the second user 106 b, andthe like.

The communication networks, such as the local area network 118 a and thewide area network 118 b, may comprise suitable logic, circuitry, andinterfaces that may be configured to facilitate communication betweendifferent components, systems and/or sub-systems of the networkenvironment 100. In accordance with an embodiment, the edge resources102 may be connected to the local area network 118 a to facilitatecommunication with each other and/or other remote networks or resources,such as the cloud resources 108, via the wide area network 118 b. Invarious embodiments, the network environment 100 may be implementedusing any number or type of communication networks.

The local area network 118 a and the wide area network 118 b may beconfigured to provide a plurality of network ports and a plurality ofcommunication channels for transmission and reception of communicationdata. The communication data may correspond to data received and/orexchanged, via the local area network 118 a and the wide area network118 b, among the edge resources 102 and the cloud resources 108. Eachnetwork port may correspond to a virtual address (or a physical machineaddress) for transmission and reception of the communication data. Forexample, the virtual address may be an Internet Protocol version 4(IPV4) or an Internet Protocol version 6 (IPV6) address, and thephysical address may be a media access control (MAC) address. Thecommunication data may be transmitted or received via a communicationprotocol, the examples of which may include, but are not limited to, ashort-range communication protocol, a Hypertext Transfer Protocol(HTTP), a File Transfer Protocol (FTP), a Simple Mail Transfer Protocol(SMTP), a Domain Name Server (DNS) protocol, and a Common ManagementInformation Protocol (CMIP) Over Transmission Control Protocol/InternetProtocol TCP/IP (CMOT).

The communication data may be transmitted or received via at least onecommunication channel of a plurality of communication channels. Thecommunication channels may include, but are not limited to, a wirelesschannel, a wired channel, or a combination of wireless and wired channelthereof. The wireless or wired channel may be associated with a datastandard which may be defined by one of a Local Area Network (LAN), aPersonal Area Network (PAN), a wireless personal LAN (WPLAN), a WirelessLocal Area Network (WLAN), a Wireless Sensor Network (WSN), a WAN, and aWireless Wide Area Network (WWAN), the Internet, cellular networks,Wireless Fidelity (Wi-Fi) networks, short-range networks (for example,Bluetooth® or ZigBee®), and/or any other wired or wireless communicationnetworks or mediums. In accordance with an embodiment, the wired channelmay be selected based on the bandwidth criteria. For example, an opticalfiber channel may be used for a high bandwidth communication, and acoaxial cable (or Ethernet-based communication channel) may be used formoderate bandwidth communication. In accordance with variousembodiments, any, some, combination, or all of the systems, engines,and/or sub-systems of the network environment 100 may be adapted toexecute any operating system, such as Linux-based operating systems,UNIX-based operating systems, Microsoft Windows, Windows Server, MacOS,Apple iOS, Google Android, or other customized and/or proprietaryoperating system. The systems, engines, and/or sub-systems of thenetwork environment 100 may be adapted to execute such operating systemsalong with virtual machines adapted to virtualize execution of aparticular operating system.

It should be noted that the communication between the various devices,systems and/or sub-systems, i.e., the CMS 110, the edge resources 102,and the cloud resources 108, of the network environment 100 may bedynamically established based on auto-selection of the most optimalnetwork from various available communication networks provided by thelocal area network 118 a and/or the wide area network 118 b. The mostoptimal network may be automatically selected from the various availablecommunication networks based on a plurality of network parameters. Nonlimiting examples of the plurality of network parameters may includelink/signal quality, bandwidth, power, cost, throughput, security level,interference level, received signal strength, Quality of Service (QoS),network loads, distance, network connection time, network selectionpolicy, and the like. In case of any adverse or untoward event, such asnetwork failure or degraded network performance, the next optimalnetwork may be dynamically selected in a seamless manner such that theestablished communication is not interrupted.

It should be noted that FIG. 1 is described herein as containing orbeing associated with a plurality of devices, systems and/orsub-systems. Nevertheless, not all the devices, systems and/orsub-systems illustrated within the network environment 100 of FIG. 1 maybe utilized in each alternative implementation of the presentdisclosure. Additionally, one or more of the devices, systems and/orsub-systems described in connection with the examples of FIG. 1 may belocated external to network environment 100. Further, certain systemsand/or sub-systems illustrated in FIG. 1 may be combined with othercomponents, as well as used for alternative or additional purposes inaddition to those purposes described herein. Furthermore, certaindevices and/or components illustrated in FIG. 1 may operate asstandalone devices or may be integrated with, embedded within, orattached to other components. Accordingly, it should be noted that thenetwork environment 100 of FIG. 1 may be implemented with any aspect ofthe various embodiments described throughout this disclosure.

In operation, a secure communication channel may be established as theapplication program 112 is downloaded and installed at the plurality ofelectronic devices 104. The CMS 110 may receive detailed information,pertaining to the plurality of users 106, from the plurality ofelectronic devices 104, via the secure communication channel. Thedetailed information may be captured at the plurality of electronicdevices 104 through a user interface of the application program 112presented at each of the plurality of electronic devices 104 duringregistration. The CMS 110 may generate a plurality of user profilesbased on the detailed information, pertaining to the plurality of users106, received from the plurality of electronic devices 104. Each of theplurality of user profiles includes a name, an age, a location, a familysize, an income, a work indicator, a preference of living environment, abrowsing history, one or more deep learning factors derived frompictures or visual descriptions, or any combination thereof. The CMS 110may store the plurality of user profiles in a user profile database andone of the plurality of users 106, such as the first user 106 a, may beenabled to select a user profile of another user, such as a second userprofile of the second user 106 b, for initiating a personal orprofessional relationship.

In accordance with an embodiment, the CMS 110 may be configured toenable the exchange of the plurality of messages between the firstelectronic device 104 a and the second electronic device 104 b prior tothe personal interaction between the first user 106 a and the seconduser 106 b. In accordance with an embodiment, for the selection of thesecond user profile of the second user 106 b by the first user 106 a,the CMS 110 may recommend the second user profile of the second user 106b to the first user 106 a via the user interface of the applicationprogram 112 presented at the first electronic device 104 a. The seconduser profile of the second user 106 b may be recommended based on aplurality of options presented by the application program 112 at theuser interface and selected by the first user 106 a based on userpreferences of the first user 106 a. In accordance with anotherembodiment, the CMS 110 may search the second user profile of the seconduser 106 b from the plurality of user profiles based on one or moresearch terms provided by the first user 106 a at the user interface ofthe application program 112 presented at the first electronic device 104a. In accordance with another embodiment, the CMS 110 may generaterating factors for other users from the plurality of users 106 based onone or more machine learning models. The rating factor may comprise aprobability of a user interaction of the first user 106 a with anotheruser (i.e., the second user 106 b) from the plurality of users 106. TheCMS 110 may recommend the second user profile of the second user 106 bbased on ranking of the rating factors for the other users from theplurality of users 106.

In accordance with an embodiment, the CMS 110 may receive, via thesecure communication channel, a consent request from the firstelectronic device 104 a associated with the first user 106 a based on aselection of the second user profile of the second user 106 b by thefirst user 106 a. The consent request may correspond to a mutualagreement to perform one or more activities during the personalinteraction between the first user 106 a and the second user 106 b. TheCMS 110 may transmit, via the secure communication channel, the consentrequest to the second electronic device 104 b. The CMS 110 may receive,via the secure communication channel, the consent response from thesecond electronic device 104 b based on options selected or dataprovided by the second user 106 b on the consent request generated bythe first user 106 a.

Once the consent request is accepted, a plurality of messages may beexchanged between the first electronic device 104 a and the secondelectronic device 104 b, via the secured communication channel, prior tothe personal interaction between the first user 106 a and the seconduser 106 b. Each of the plurality of messages may compriseidentification information associated with the first user 106 a and thesecond user 106 b, and a payload comprising a plurality of text-basedmessages, voice-based messages, and video messages.

In accordance with an embodiment, the CMS 110 may determine an eventbased on analysis of the plurality of messages using natural languageprocessing techniques. Corresponding to the event, a first set of tasksmay be generated for the personal interaction between the first user 106a and the second user 106 b. The first set of tasks generatedcorresponding to the event may comprise at least generating a calendarentry for both of the first user 106 a and the second user 106 b forscheduling the personal interaction and booking a meeting venue for thepersonal interaction.

In accordance with an embodiment, the CMS 110 may enable the first user106 a and the second user 106 b for a direct exchange of one or moreidentity confirmation messages presented on corresponding userinterfaces of an application program presented at the first electronicdevice 104 a and the second electronic device 104 b respectively, duringor prior to the personal interaction between the first user 106 a andthe second user 106 b.

The CMS 110 may receive, during a personal interaction between the firstuser 106 a and the second user 106 b, uttered speech signals and sensordata of at least the first user 106 a from at least the first electronicdevice 104 a, via the secure communication channel. The personalinteraction may be scheduled based on the consent response, receivedfrom the second electronic device 104 b, corresponding to the acceptanceof the consent request received from the first electronic device 104 a.In accordance with an embodiment, the uttered speech signals may beextracted from a conversation between the first user 106 a and thesecond user 106 b during the personal interaction.

The CMS 110 may identify a current set of user characteristics based ontime sequence-based user interactions and the uttered speech signals ofeach user during the personal interaction. The current set of usercharacteristics may be utilized as a training data set for theprediction of the new set of user characteristics. The CMS 110 maypredict a new set of user characteristics for the first user 106 a andthe second user 106 b during the personal interaction based on timesequence-based personal interaction, the training data set, and anartificial neural network model. In accordance with an embodiment, theprediction of the new set of user characteristics may be further basedon social media data of the first user 106 a and the second user 106 bcollected from one or more public information databases. The socialmedia data may include a plurality of media shared, content posts,social media contacts having a predefined social media distance betweenuser accounts and information relating to the social media contacts.

The CMS 110 may determine a confidence score based on an intent of bothof the first user 106 a and the second user 106 b, current sensor dataand a new set of user characteristics predicted for the first user 106 aand the second user 106 b during the personal interaction. The CMS 110may further detect an immediate consent or an immediate dissent of oneof the first user 106 a or the second user 106 b at a defined timestampduring the personal interaction based on at least one of a comparison ofthe confidence score with a threshold value, one or more explicit orimplied keywords from the uttered speech signals, and an extent ofdeviated values of the sensor data. The CMS 110 may further validate theimmediate consent or the immediate dissent of one of the first user 106a or the second user 106 b based on a plurality of criteria. Inaccordance with an embodiment, the plurality of criteria for thevalidation of the immediate consent or dissent of the first user 106 aor the second user 106 b may comprise at least informed, freely given,reversible, enthusiastic, and specific agreement between the first user106 a and the second user 106 b to perform one or more activities duringthe personal interaction.

In accordance with an embodiment, the CMS 110 may override theacceptance on the consent request by the immediate dissent one of thefirst user 106 a or the second user 106 b for performing an activityduring the personal interaction. The CMS 110 may perform a second set oftasks based on the validation of the immediate dissent of one of thefirst user 106 a or the second user 106 b. In accordance with anembodiment, the CMS 110 may publish, based on a user request, a timesequence-based recording of conversation incurred during the personalinteraction. The published time sequence-based recording of theconversation incurred during the personal interaction may correspond toone of the second set of tasks. Further, other tasks from the second setof tasks performed based on the validation of the immediate dissent ofone of the first user 106 a or the second user 106 b may comprisedialing an emergency number of current location, dialing a number of anemergency contact person, or activating an emergency alarm sound.

In accordance with an embodiment, it may be required that both of theelectronic devices of the two users engaged in the personal interactionare equipped with the application program 112. In such embodiment, theproposed solution for consent detection and validation may beimplemented in the best possible manner providing equal operationalfacilities and complete functionalities to both the users. However, inaccordance with another embodiment, even if one of the two electronicdevices of the two users engaged in the personal interaction is equippedwith the application program 112, the proposed solution for consentdetection and validation may still be provided but with limitedoperational facilities and functionalities available to only one of thetwo users. For example, only one user having the electronic deviceequipped with the application program 112 can avail the functionalitiesof the first and the second set of tasks, as described herein, thoughthe task of time sequence-based conversation report published by the CMS110 may be available to both the users.

It should be noted that in certain instances, one or more of thedevices, components, data processing engines, systems and/or sub-systemsmay be implemented as the combination of edge resources 102 and thecloud resources 108 connected to each other via the combination of thelocal area network 118 a and the wide area network 118 b. However, inother instances, certain devices, systems and/or sub-systems may beincluded within or as a portion of one or more of the other describedsystems, as well as other systems, though not described in theillustrated implementation, yet not deviating from the scope of thedisclosure.

FIG. 2A is a block diagram 200A that illustrates an exemplary electronicdevice 202, in accordance with an exemplary embodiment of the presentdisclosure. FIG. 2A is described in conjunction with FIGS. 2B and 2Cthat depict two views of a display unit of the exemplary electronicdevice 202, in accordance with an exemplary embodiment of the presentdisclosure.

With reference to FIG. 2A, the structure and functionality of theexemplary electronic device 202 may correspond to the one of each of theplurality of electronic devices 104. The exemplary electronic device 202may include various components, such as a first network interface 204, afirst processor 214, a first memory 206, a sensing unit 216, input unit218, and an output unit 220. The first memory 206 may include a firstrandom-access memory (RAM) 208, a first read only memory (ROM) 212, afirst program data 210, and the application program 112. In accordancewith various embodiments disclosed herein, the first electronic device104 a and the second electronic device 104 b may correspond to theexemplary electronic device 202.

The first network interface 204 may comprise suitable logic, circuitry,and interfaces that may be configured to facilitate a communication withdifferent external devices, such as the CMS 110, a remote server, oranother electronic device, connected to the exemplary electronic device202. The first network interface 204 may include, for example, awired/wireless headset port, an external-charger port, a wired/wirelessdata port, a memory card port, an audio input/output (I/O) port, a videoI/O port, and an earphone port. In response to a connection between anexternal device and the first network interface 204, appropriatelaunches or corresponding drivers related to the connected externaldevice may be performed.

The first network interface 204 may include a communication interfacethat may be configured to communicate wireless signals and wireless datareceived from the external electronic devices, such as the CMS 110, aremote server, or another electronic device, to the first processor 214.In accordance with various embodiments, the communication interface mayinclude, for example a mobile communication module, a broadcastreception module, a wireless Internet module, a short-rangecommunication module, and a location information module.

The mobile communication module may transmit and receive wirelesssignals to perform data communication with a base station, an externalterminal, and/or a server over a mobile communication network. Themobile communication network may be established according to variouscommunication methods, for example, the Global System for Mobilecommunication (GSM), code-division multiple access (CDMA), code-divisionmultiple access 2000 (CDMA2000), Enhanced Voice-Data Optimized orEnhanced Voice-Data Only (EV-DO), WCDMA, High Speed Downlink PacketAccess (HSDPA), High Speed Uplink Packet Access (HSUPA), Long TermEvolution (LTE), Long Term Evolution-Advanced (LTE-A), thefifth-generation (5G) technology standard (and all the successortechnology standards, such as 6G, 7G, and beyond) for broadband cellularnetworks, and the like. The broadcast reception module may receive abroadcast signal and/or broadcast-related information from an externalbroadcast management server through a broadcast channel.

The wireless Internet module may transmit and receive wireless signalsfor data communication over a network according to wireless Internettechnologies, for example, WLAN, Wi-Fi, Wi-Fi Direct, Digital LivingNetwork Alliance (DLNA), wireless broadband (WiBro), WorldInteroperability for Microwave Access (WiMAX), HSDPA, HSUPA, LTE, LTE-A,5G technology standard for broadband cellular networks, and the like.The short-range communication module may transmit and receive wirelesssignals for data communication over a limited range using variousshort-range communication technologies, for example, Bluetooth©,radio-frequency identification (RFID), Infrared Data Association (IrDA),ultra-wideband (UWB), ZigBee®, near-field communication (NFC), Wi-Fi,Wi-Fi Direct, Wireless Universal Serial Bus (Wireless USB) technologies,and the like. The location information module may determine the currentlocation of the exemplary electronic device 202 using various modules,such as a Global Positioning System (GPS) module or a Wi-Fi module.

The first memory 206 may comprise suitable logic, circuitry, andinterfaces that may be configured to store data supporting variousfunctions of the exemplary electronic device 202. The first memory 206may store a number of application programs or applications (for examplethe application program 112) running on the exemplary electronic device202, data for operation of the exemplary electronic device 202, andcommands. The first memory 206 may store information and/or instructionsfor use in combination with the first processor 214. The first memory206 may include volatile and non-volatile memory, such as the first RAM208 and the first ROM 212. A number of program modules may be stored onthe hard disk, external disk, the first ROM 212 or the first RAM 208,including an operating system (not shown), one or more applicationprograms, such as the application program 112, other program modules(not shown), and the first program data 210. The application program 112may include at least a part of the functionality as described above. Thefirst RAM 208 may be of any type, such as Static RAM (SRAM), Dynamic RAM(DRAM), or Synchronous Dynamic RAM (SDRAM). A basic input/output system(BIOS) containing the basic routines that helps to transfer informationbetween elements within the exemplary electronic device 202, such asduring start-up, may be stored in the first ROM 212.

The first processor 214 may comprise suitable logic, circuitry, andinterfaces that may be configured to determine an executable operationof the exemplary electronic device 202 based on executable instructionsstored in the first memory 206 or commands provided by the user. Thefirst processor 214 may be configured to sense, extract, and detect,collect data and/or receive speech signals for a data analysis andmachine learning operation, through the sensing unit 216 and the inputunit 218 in the exemplary electronic device 202. Accordingly, the firstprocessor 214 may collect information for processing, storing in thefirst memory 206 or transmitting to the external devices, such as theCMS 110, a remote server, or another electronic device, connected to theexemplary electronic device 202, via the local area network 118 a and/orthe wide area network 118 b.

In accordance with an embodiment, the computing functionalities of thefirst processor 214 in the exemplary electronic device 202 disclosedherein may be implemented in one or more silicon cores in a reducedinstruction set computing (RISC) processor, an ASIC processor, a complexinstruction set computing (CISC) processor, FPGAs, and othersemiconductor chips, processors, or control circuits.

It should be noted that the terms “processor” or “microprocessor”referred in FIG. 2A include not only a traditional microprocessor (suchas Intel's® industry-leading x86 and x64 architectures), but alsographics processors, matrix processors, and any ASIC, FPGA,microcontroller, digital signal processor (DSP), programmable logicdevice, programmable logic array (PLA), microcode, instruction set,emulated or virtual machine processor, or any similar device,combination of devices, or logic elements (hardware or software) thatpermit the execution of instructions.

The sensing unit 216 may comprise suitable logic, circuitry, andinterfaces that may be configured to detect (or sense) information abouta user (associated with the exemplary electronic device 202) and thesurrounding environment around the exemplary electronic device 202. Forexample, the sensing unit 216 may include a proximity sensor, a touchsensor, an illuminance sensor, an acceleration sensor, a magneticsensor, a gravity sensor, a gyroscope sensor, a motion sensor, an RGBsensor, an infrared sensor, an ultrasonic sensor, and a battery gauge.The sensing unit 216 may further include biometric sensors, such asretina scanner, fingerprint and thumbprint scan sensor, optical scanner,the microphone, to detect biometric data of the user.

The biometric data may include morphological biometric datacorresponding to user identification metrics and measurement of physicaltraits and body structure of the associated user. Non-limiting examplesof the morphological biometric data may include, voice identification,iris identification, retinal identification, facial identification,fingerprint identification, finger geometry recognition, veinrecognition, hand geometry, ear recognition, odor recognition, orelectroencephalogram-based identification, and/or the like.

The biometric data may further include bio-signals corresponding tomeasurement of psychophysical characteristics or parameters andmovement-related metrics of the user. Non-limiting examples of thepsychophysical characteristics or parameters may include blood pressure,heart rate, pulse rate, body temperature, oxygen level, perspiration,electro dermal activity, brain waves, and/or the like. Non-limitingexamples of the movement-related metrics may include distance moved,speed of movement, time spent, and/or the like.

The biometric data may include behavioral biometric data correspondingto measurement of behavioral identifiers unique to a user. Non-limitingexamples may include signatures, gait biometrics, keystroke recognition,hand expressions, facial expressions, vocal expressions, body gestures,eye tracking, and/or the like.

The biometric data may further include biological biometric datacorresponding to biological measurements of the user at the genetic ormolecular level. Biological biometric data may require sampling of DNAfrom blood, nails, hair, buccal swabs, or bodily fluids for DNAmatching, diagnosing genetic diseases, and microbiological studies, thedetailed records of which may be stored in the first memory 206 of theexemplary electronic device 202. In accordance with an embodiment, thebiological biometric data may be received from DNA biosensors and genechips that may provide sequence-specific information in a quicker,easier and economical manner compared to the traditional hybridizationmethods. In accordance with another embodiment, the biological biometricdata may be received from microchips implanted in the body of a user ofthe exemplary electronic device 202.

In accordance with an embodiment, such biological biometric data may becompared with reference biological biometric data stored locally withinthe first memory 206 of the exemplary electronic device 202. Inaccordance with another embodiment, the reference biological biometricdata may be fetched from another application program, such as a healthmonitoring app, installed in the first memory 206 of the exemplaryelectronic device 202. In accordance with another embodiment, thereference biological biometric data may be fetched from a remote medicalrepository communicatively coupled with the exemplary electronic device202, via the local area network 118 a and/or the wide area network 118b.

The input unit 218 may comprise suitable logic, circuitry, andinterfaces that may be configured to receive an input associated withthe user which may be further analyzed to be processed based on controlcommand provided by the user. For example, a camera may be embeddedwithin the exemplary electronic device 202 for capturing an image signalwhen triggered by the user. The camera may further process image frames,such as still images, video, or the like, acquired by an image sensor ina video call mode or a shooting mode. The processed image frame may bedisplayed on a display unit of the output unit 220 or stored in thefirst memory 206. In another example, a microphone may be used forreceiving speech signals uttered by one or more users and implementingnoise-removal algorithms to further refine the uttered speech signal byremoving background noise. The microphone may process the uttered speechsignals into electrical speech data. The processed speech signals or rawspeech signals may be stored in the first memory 206 or may betransmitted to external electronic devices, such as the CMS 110, theremote server, or another electronic device. Other examples may include,but are not limited to, a touch input unit and a mechanical input unit(or a mechanical key, for example, a button, a dome switch, a jog wheel,a jog switch, and the like located at the exemplary electronic device202). The touch input unit may include a virtual key, a soft key, or avisual key displayed on a touch screen through software processing ormay include a touch key placed on a portion other than the touch screen.

The output unit 220 may comprise suitable logic, circuitry, andinterfaces that may be configured to render an output related to varioussenses, such as visual, auditory, tactile, and the like. Numerousexamples of the output unit 220 may include a display unit 220 a, asound output unit 220 b, a haptic unit 220 c, an optical unit 220 d, andthe like. The display unit may be configured to display informationprocessed by the exemplary electronic device 202. For example, thedisplay unit may present screen information of the application program112 being executed on the exemplary electronic device 202, or UserInterface and Graphic User Interface information according to theexecution-screen information. In accordance with an embodiment, thedisplay unit 220 a and a touch sensor may have an inter-layeredstructure or may be integrated, thereby implementing a touch screen. Thetouch screen may serve as the user input/output unit that provides aninput interface between the exemplary electronic device 202 and the userand provides an output interface between the exemplary electronic device202 and the user.

As depicted in a first view 200B in FIG. 2B, an exemplary first userinterface is displayed on the display unit 220 a that includes aplurality of icons, for example, messages icon 224 a, a socialnetworking site icon 224 b, games icon 224 c, contact list icon 224 d,calendar icon 224 e, settings icon 224 f, and an icon for theapplication program 112. As the first user 106 a selects the applicationprogram 112, an exemplary second user interface is displayed on thedisplay unit 220 a, as depicted in a second view 200C in FIG. 2C. Theexemplary second user interface is displayed on the display unit 220 athat includes another plurality of icons, for example, user profile icon226 a, recommended profiles icon 226 b, consent request profile icon 226c, consent response profile icon 226 d, task notifications icon 226 e,report generation icon 226 f, and the like. The user profile icon 226 apresents a user interface to create a first user profile of the firstuser 106 a or edit the existing first user profile of the first user 106a. The recommended profiles icon 226 b presents a user interface to. Theconsent request profile icon 226 c presents a user interface to view theopen consent requests, i.e., the ones that have been generated and theone that is being generated. The consent response profile icon 226 dpresents a user interface to view the consent responses received fromone or more users or the ones that are under negotiation. The tasknotifications icon 226 e presents a user interface to view the first andthe second set of tasks generated by the CMS 110. The report generationicon 226 f presents a user interface to view and generate, based on auser request, a time sequence-based recording of conversation incurredduring the personal interaction that are published by the CMS 110.

The sound output unit 220 b may output audio data received from thefirst network interface 204 or stored in the first memory 206, in a callsignal reception mode, a call mode, a recording mode, a speechrecognition mode, broadcast reception mode, or the like. The soundoutput unit 220 b may include at least one among a receiver, a speaker,and a buzzer. The haptic unit 220 c may generate various tactileeffects, for example a vibration, that the user can feel. The opticalunit 220 d may output a signal for notifying the occurrence of an event,by using light of a light source of the exemplary electronic device 202.Examples of the event generated in the exemplary electronic device 202may include receiving a message, receiving a call signal, a missed call,an alarm, schedule notification, receiving an email, receivinginformation through an application, and the like.

It should be noted that all or parts of hardware components of theexemplary electronic device 202 disclosed herein may readily be providedin a system-on-a-chip (SoC), including a central processing unit (CPU)package. In accordance with an embodiment, the SoC may correspond to anintegrated circuit (IC) that integrates components of a computer orother electronic system into a single chip. The SoC may contain digital,analog, mixed-signal, and radio frequency functions, all of which may beprovided on a single chip substrate. Other embodiments may include amulti-chip-module (MCM), with multiple chips located within a singleelectronic package and configured to interact closely with each otherthrough the electronic package.

FIGS. 3A and 3B depict block diagrams 300A and 300B, respectively, thatcollectively illustrate the various components and data processingengines of the CMS 110 for consent detection and validation, inaccordance with an exemplary embodiment of the present disclosure. Withreference to the block diagram 300A, there is shown the CMS 110comprising a second network interface 302 and a second memory 304 thatincludes a second RAM 306, a second ROM 308, a second program data 310,and the application program 112. The CMS 110 may further comprise asecond processor 312, a machine learning (ML) engine 314, an artificialintelligence (AI) engine 316 (further comprising an evaluation engine316 a and a recommendation engine 316 b), an automatic speechrecognition (ASR) system 318, a natural language processing (NLP) engine320, a user recognizer 322, a consent detection engine 324, a consentvalidation engine 326, a task manager 328, an alert engine 330, a userprofile database 332, a task list database 334, a consent database 336,and an additional database 338. With reference to the block diagram 300Bin FIG. 3B, there are shown an acoustic frontend 340, a speechrecognition engine 342, an acoustic model 344, a token manager 346, apronunciation dictionary 348, and a language model 350 in the ASR system318 of the CMS 110. There are further shown a morpheme analyzer 352, agrammar module 354, a syntax parser 356, and an intent analyzer 358 inthe NLP engine 320 of the CMS 110.

The second network interface 302 may be configured to transmit/receivethe information over the network, such as the local area network 118 aand/or the wide area network 118 b, to/from other network interfaces ofother devices. The second network interface 302 may include wiredcommunication interfaces, wireless communication interfaces, cellularcommunication interfaces, and other communication interfaces to providecommunication via other modalities. When used in a LAN networkingenvironment, the CMS 110 may be connected to LAN through a networkinterface or adapter in the second network interface 302. When used in aWAN networking environment, the CMS 110 may include a modem in thesecond network interface 302 or other means for establishingcommunications over WAN, such as Internet or other type of computernetwork. Various well-known protocols, such as, transmission controlprotocol/Internet protocol (TCP/IP), Ethernet, FTP, HTTP, and the likemay be used. Accordingly, the CMS 110 may be operated in a client-serverconfiguration to permit the plurality of electronic devices 104 toretrieve web pages from a web-based server or the application program112. The communication technology used by the second network interface302 may include GSM, CDMA, LTE, 5G, WLAN, Wi-Fi, Bluetooth® RFID, IrDA,ZigBee®, NFC, and the like.

In accordance with an embodiment, the second network interface 302 maybe configured to optionally obtain context information associated withthe speech signals from the plurality of electronic devices 104 with orimmediately after the speech signals are received. The contextinformation may include user specific data, vocabulary, and/orpreferences related to receipt of the speech signals. In accordance withan embodiment, the context information may include information about thesoftware and hardware state of the plurality of electronic devices 104at the time the user request is received, and/or the ambient environmentaround the plurality of electronic devices 104 at the time the speechsignals are received.

The second memory 304 may comprise suitable logic, circuitry, andinterfaces that may be configured to store data supporting variousfunctionalities of the CMS 110. The second memory 304 may store a numberof application programs or applications (for example the applicationprogram 112) hosted by the CMS 110, and data and commands for variousoperations of the CMS 110. The second memory 304 may store informationand/or instructions that may be executed by the second processor 312.The second memory 304 may include volatile and non-volatile memory, suchas the second RAM 306 and the second ROM 308. A number of programmodules may be stored on the hard disk, external disk, the second RAM306 or the second ROM 308, including an operating system (not shown),one or more application programs, such as the application program 112,other program modules (not shown), and a second program data 310. Thesecond RAM 306 may be of any type, such as SRAM, DRAM, or SDRAM. A BIOScontaining the basic routines that helps to transfer information betweenelements within the CMS 110, such as during start-up, may be stored inthe second ROM 308.

The second processor 312 may comprise suitable logic, circuitry, andinterfaces that may be configured to determine an executable operationof the CMS 110 based on executable instructions stored in the secondmemory 304 or commands provided by the user. The second processor 312may be configured to sense, extract, and detect, collect data and/orreceive speech signals from the exemplary electronic device 202 for adata analysis and machine learning operation. Accordingly, the secondprocessor 312 may collect information for processing, storing in thesecond memory 304 or transmitting to the external devices, such as theplurality of electronic devices 104, a remote server, or anothercomputing device, connected to the CMS 110, via the local area network118 a and/or the wide area network 118 b.

In accordance with an embodiment, the second processor 312 may performvarious functions for implementing intelligent emulation (specifically,a knowledge-based system, an inference system, and a knowledgeacquisition system) for the CMS 110. This may be applied to severaltypes of systems (for example, a fuzzy logic system) including anadaptive system, a machine learning system, an artificial neuralnetwork, and the like. In accordance with an embodiment, the secondprocessor 312 may control the overall operation of the exemplary CMS110, in addition to the operations related to the application program112. The second processor 312 may process speech signals, data and/orinformation, or may execute the application program 112 stored in thesecond memory 304 through the above-described elements, therebyproviding or processing information or a function or performing a taskappropriate for each of the plurality of users 106. To this end, thesecond processor 312 may request, receive, and/or use data of the MLengine 314, and may control the CMS 110 so that a predicted operation oran operation determined to be preferable, of an executable operation isperformed. In accordance with an embodiment, the computingfunctionalities of the second processor 312 in the CMS 110 disclosedherein may be implemented in one or more silicon cores in a RISCprocessor, an ASIC processor, a CISC processor, FPGAs, and othersemiconductor chips, processors, or control circuits.

The ML engine 314 may comprise suitable logic, circuitry, and interfacesthat may be configured to provide CMS 110 an ability to automaticallylearn and improve from experience without being explicitly programmed.The ML engine 314 may implement one or more machine learning algorithmsthat may be configured to make inferences and determinations about thecurrent workflow scenario in view of feedback received about the currentworkflow scenario and information provided by one or more informationsources and/or the historical data referenced from the databases. Inaccordance with an embodiment, the ML engine 314 may facilitate theimplementation of the AI engine 316 to automatically make determinationsand inferences in each workflow and optimize the performance of the CMS110 in real-time.

The ML engine 314 may be configured to receive, classify, store, andoutput information to be used for data mining, a data analysis,intelligent decision making, and machine learning algorithm andtechnology. In accordance with an embodiment, the ML engine 314 may beimplemented by using the databases maintained at the CMS 110communicating with the exemplary electronic device 202. In accordancewith another embodiment, the ML engine 314 may be implemented by using amemory maintained as one of the cloud resources 108 in a cloud computingenvironment, or other remote memory location that is accessible by theCMS 110 through a communication network, such as the local area network118 a and/or the wide area network 118 b. Generally, the ML engine 314may be configured to store data in one or more databases to identify,index, categorize, manipulate, store, find, and output for use insupervised learning, unsupervised learning, reinforcement learning, datamining, a predictive analysis, and/or the like. The information storedby the ML engine 314 may be used by the second processor 312 or one ormore controllers of the CMS 110, using any of several types of dataanalysis algorithms and machine learning algorithms. Examples of suchalgorithms may include, but are not limited to, a k-nearest neighborsystem, fuzzy logic (for example, possibility theory), a classifier tree(for example, a perceptron tree, a support vector tree, a Bayesiannetwork, a Petri net (for example, a finite state machine, a mealymachine, and a Moore finite state machine), a random forest, a Markovtree, a decision tree forest, a neural network, a Boltzmann machine,vector quantization, a pulsed neural network, a support vector machine,a maximum margin classifier, hill climbing, an inductive logic system,pandemonium model and system, artificial fusion, sensor fusion, imagefusion, reinforcement learning, augmented reality, pattern recognition,automated planning, and the like.

In accordance with an embodiment, the ML engine 314 may invoke one ormore machine learning models to be applied to learning over timebehaviors or biometric data of the plurality of users 106communicatively coupled with the CMS 110 through the plurality ofelectronic devices 104. In one aspect, the plurality of electronicdevices 104 comprising various sensors, such as microphone, voicecapturing endpoint, retina scanner, heart monitor, video camera, and thelike, may be used to capture speech, emotional data, biometric data,and/or psychophysical characteristics or parameters (for example,electro dermal activity, heart rate, blood pressure, and the like data.

The AI engine 316 may comprise suitable logic, circuitry, and interfacesthat may be configured to learn from experience, adjust to new inputsand perform human-like intelligent tasks using output of the ML engine314, thereby reducing or mitigating adverse outcomes of the CMS 110. Inthis regard, using one or more types of machine learning algorithms andthe databases, the AI engine 316 may learn all aspects of how theworkflow should be organized to achieve optimal operational andfinancial outcomes. The AI engine 316 may apply intelligent reasoningbased on the speech signals and sensor data and generate educateddecisions based on such reasoning.

The AI engine 316 may include an evaluation engine 316 a configured toevaluate the activities performed during a personal interaction betweenthe first user 106 a and the second user 106 b. For example, theevaluation engine 316 a may evaluate the personal interaction toidentify events and generate observations about the personal interactionto facilitate making more intelligent and informed decisions. Inaccordance with an embodiment, the evaluation engine 316 a may performvarious functionalities, such as event identification and riskassessment.

The event identification may include an event reflected in the personalinteraction correlated with the sensor data, which warrants immediateattention and/or a rapid response. In general, the event may correspondto change in state, condition, or status of the behaviour and/or usercharacteristics of the first user 106 a and/or the second user 106 bdetermined from the corresponding uttered speech signals and sensordata, which warrants attention and/or a response.

The event may be defined as an occurrence of an incident during thepersonal interaction between the first user 106 a and the second user106 b that is desired or undesired. For example, a desired event may befixing date/time/venue for the personal interaction. An undesired eventmay be inability to perform an activity during the personal interaction.In accordance with an embodiment, from one perspective, an occurrence ofan improper or an undesired performance of an activity contradictory tothe consent request, a failure to perform an aspect of the activity, ordemonstration of a level of fatigue or attentiveness may also beconsidered as the event.

It should be noted that the events described above are merely exemplaryand many additional types of events associated with a specificapplication area may be considered that warrants immediate attention.Further, it should be noted that the events may involve a combination ofdifferent parameters associated with the users, the sensor data, theactivities permissible and non-permissible in accordance with theconsent request and/or the data corresponding to the surroundingenvironment around the first electronic device 104 a and the secondelectronic device 104 b.

The evaluation engine 316 a may perform risk assessment to determinewhether the event should be considered significant or not. In accordancewith an embodiment, the evaluation engine 316 a may be configured todetermine a risk score for the identified event. Accordingly, the eventmay be classified as significant if corresponding risk score exceeds adefined threshold value. In accordance with another embodiment, theevent may be classified as a low-risk if corresponding risk score isbetween a first set of defined threshold values, a medium-risk ifcorresponding risk score is between a second set of defined thresholdvalues, or a high-risk event if corresponding risk score is between athird set of defined threshold values. In accordance with yet anotherembodiment, the events may be classified as warranting attention oracknowledgment verses events warranting an immediate response based onthe magnitude of the risk score, such as low-risk score and high-riskscore, respectively. It should be noted that other classificationschemes including additional categories of risk can be realized, withoutdeviation from the scope of the disclosure.

The AI engine 316 may further include a recommendation engine 316 bconfigured to recommend an action to be taken corresponding to theidentified event. The recommendation engine 316 b may be configured todetermine or infer and provide one or more responses as therecommendation to the identified events. In accordance with anembodiment, based on the recommendation, a first set of tasks may begenerated. Examples of the first set of tasks may include, but are notlimited to, dialing an emergency number of current location, generatinga calendar entry, booking a venue, and the like. In accordance withanother embodiment, based on the recommendation, a second set of tasksmay be generated. Examples of the second set of tasks may include, butare not limited to, publishing a time sequence-based conversationreport, dialing an emergency number of current location, dialing anumber of an emergency contact person, activating an emergency alarmsound, and the like.

The ASR system 318 may comprise suitable logic, circuitry, andinterfaces that may be configured to receive natural language speechcorresponding to the uttered speech signals through the second networkinterface 302, process the speech signals, and generate a recognitionresult which is a machine-readable version of the speech signals. Forcomprehending the natural language or human speech, Natural LanguageProcessing (NLP)-Based ASR system is widely used. An example of themachine-readable version of the speech signals may be text, which mayinclude words, word strings, phrases, sentences, or other forms ofverbal expression. In accordance with different embodiments, the ASRsystem 318 may reside entirely or in part in one of the cloud resources108 in cloud-based environment and/or the CMS 110.

The acoustic frontend 340 may be configured to receive the speechsignals (uttered by the first user 106 a and the second user 106 bcommunicating with each other) from the exemplary electronic device 202,via the available communication networks provided by the local areanetwork 118 a and/or the wide area network 118 b. The acoustic frontend340 may be further configured to transform the received speech signalsinto data that may be processed by the speech recognition engine 342.

In accordance with an embodiment, the acoustic frontend 340 may befurther configured to process or pre-process the received speech signalsfor noise cancellation, normalization, and the like, and divide thedigitized speech signals into frames representing time intervals. Forsuch frames, the acoustic frontend 340 may determine a number of values(referred to as features that represent the qualities of the speechsignals) and a set of values (referred to as a feature vector thatrepresents the features/qualities of the speech signals within theframe).

Each feature may include, among others, a deletion, an amendment, anaddition, or a combination thereof to one of metadata or data of thespeech signals. For example, brackets in the metadata or thetranscription data of the speech signals may be deleted. The feature canalso include relationships between words, sentiment(s) (for example,anger, happiness, sadness, boredom, love, excitement, and the like),recognize speech, accent, topics (for example, sports, documentary,romance, sci-fi, politics, legal, and the like), noise profile(s),volume profile(s), and audio analysis variables.

In accordance with an exemplary embodiment, the acoustic frontend 340may perform a Fourier transform on the uttered speech signals to extractspectral features that characterize the uttered speech signals as aseries of representative multidimensional vectors. Such features orcomponents may be associated with a process of information compressionand dimension reduction which is required for speech recognition fromthe uttered speech signals. Various examples of such features orcomponents may be a differential cepstrum, a Linear Predictive Coding(LPC) cepstrum, a Perceptual Linear Prediction (PLP) cepstrum, aMel-frequency cepstral coefficients (MFCC), neural network featurevector techniques, linear discriminant analysis, semi-tied covariancematrices and the like.

The speech recognition engine 342 may comprise suitable logic,circuitry, and interfaces that may be configured to process, based on aplurality of models, the pre-processed speech signals from the acousticfrontend 340. The speech recognition engine 342 may be configured to mapthe received feature vectors from the acoustic frontend 340 to languagephonemes and words from the stored acoustic model 344 and language model350.

The speech recognition engine 342 may compute recognition scores for thefeature vectors based on the acoustic information and languageinformation. The acoustic information may be used to calculate anacoustic score representing a likelihood that the intended soundrepresented by a group of feature vectors matches a language phoneme.The language information may be used to adjust the acoustic score byconsidering what sounds and/or words are used in context with eachother. Accordingly, the likelihood of grammatically correct speechresults is improved in the automatic speech recognition process. Varioustechniques to map the feature vectors to the phonemes by the acousticmodel 344 may correspond to Hidden Markov models (HMMs) on the spectraldata or neural network (NN). In simpler words, the acoustic model 344predicts which sound, or phoneme, from the phone set is being spoken ineach frame of audio and further predicts the probability of each phenomebeing spoken in a short frame of audio. The acoustic model 344 generatescandidate phoneme sequences and corresponding acoustic scores. Inaccordance with an exemplary embodiment, deep neural networks trained onthousands of hours of transcribed audio data may be used for theacoustic model 344. Various factors, such as accent, gender, age,microphone, variation in enunciation, and background noise may bemodeled by the acoustic model 344.

The token manager 346 may be configured to check the candidate phonemesequences against a pronunciation dictionary 348 that includes multiplepronunciations for each word. Accordingly, the token manager 346 maygenerate candidate token sequences made up of sequences of words thatcomprise the candidate sequence of phonemes in order.

The language model 350 may use the candidate token sequences and theacoustic scores and computes a probability score for each candidatetoken sequence. Stated differently, the language model 350 learns whichsequences of words are most likely to be spoken and predicts which wordswill follow on from the current words and with what probability. Theprobability score may be based on the frequency with which the tokensequences are likely to appear in the language, weighted by theprobability of the token sequence according to the acoustic score. Thelanguage model 350 may be trained on corpora of examples of languageusage. In accordance with an embodiment, the language model 350 mayoutput a transcription, i.e., the resultant text, with the singlehighest language model score. In accordance with an embodiment, thelanguage model 350 may output multiple candidate transcriptions, i.e.,multiple instances of the resultant text, and correspondingtranscription scores, each of which may be processed to determine themost probable intent. The language model 350 may be based on n-grammodels (which computes probabilities of sequences of N number oftokens), or NN models with long-term recurrence. Thus, the output of thelanguage model 350 may be a single textual representation of the speech,an N-best list including multiple hypotheses and respective scores,lattice, and the like.

Thus, the speech recognition engine 342 may be configured to convert thereceived speech signals to corresponding resultant text for furtheranalysis by the NLP engine 320. In other words, the speech recognitionengine 342 may enable real-time transcription of audio streamscorresponding to the uttered speech signals into the correspondingresultant text, such as sequence of characters (for example, words andstrings) or tokens. Thus, the speech recognition engine 342 may beconfigured to output recognition candidates using a defined speech modeland searching for recognizable words based on the extracted features orcomponents. In an exemplary embodiment, the speech model may beinformation on a character, a unit of phoneme and a feature of eachphoneme. Other units of the defined speech model for speech recognitionmay be a phoneme, a diphone, a triphone, a quinphone, a syllable, and aword. Various non-limiting examples, on which the speech model may bebased, may include hidden Markov model (HMM), Dynamic Time Warping(DTW), Artificial Neural Networks (ANN), and the like.

In accordance with an embodiment, the speech recognition engine 342 maybe realized as a trained speech recognition engine 342 based on AIengine 316 and the ML engine 314 for accurately processing utterancepatterns and text conversion. In accordance with an embodiment, thespeech recognition engine 342 may use modules of NLP engine 320, coupledwith the acoustic model 344, the language model 350, and thepronunciation dictionary 348 for providing optimized resultant text.

The NLP engine 320 may comprise suitable logic, circuitry, andinterfaces that may be configured to process the transcriptions, i.e.,the resultant text, from the ASR system 318 and determine the userintent. The user intent may be determined based on collaborativeoperations performed by various components, such as a morpheme analyzer352, a grammar module 354, a syntax parser 356, and an intent analyzer358 on the resultant text.

The morpheme analyzer 352 may classify the resultant text correspondingto the uttered speech signals into a morpheme unit, which is thesmallest comprehendible speech unit. Based on the result of the morphemeanalyzer 352 and language grammar from the grammar module 354, thesyntax parser 356 may segregate the resultant text into a plurality ofphrases, such as noun phrases, adjective phrases, verb phrases, thesubjects, objects, modifiers, and the like, and may determine therelationship between the each of the plurality of phrases with otherphrases from the plurality of phrases. The intent analyzer 358, based onthe speech signals, may be configured to derive an intent of the userbased on one of a plurality of intent analysis techniques. Non-limitingexamples of such intent analysis techniques may include, Named EntityRecognition that is based on grammar rules and supervisor codes,Sentiment analysis that is based on positive, negative or neutralaspects of data, Summarization of Text that is based on graph-basedcentrality scoring of sentences, Aspect Mining that is based on markingup a word in a corpus as corresponding to a particular part of speechbased on its definition and context, Topic Modelling that is based inidentification of common words across the topics, and the like.

In accordance with an embodiment, the NLP engine 320 may derive theintent of the user based on a domain, one or more tasks, and one or moreparameters required to accomplish the one or more tasks. Examples of thedomain (i.e., the field of thought, dialogue, interaction, action, topicof conversation, and the like) may include weather, sports, travel,food, restaurants, hotels, meeting, and the like. Examples of the one ormore tasks may include weather update, booking tickets for a sportsevent, booking a flight, reserving a table or a room, generating acalendar entry, and the like. Examples of the one or more parametersrequired to accomplish the one or more tasks my include (city name,date, time) for the weather update, (date, time, number of persons) forbooking tickets for the sports event, (departure date and time, numberof persons, return flight, booking class) for booking a flight, (date,time, number of persons) for reserving a table or a room, (date, time,topic) for generating a calendar entry.

The NLP engine 320 may be further configured to determine an event basedon analysis of the plurality of messages using natural languageprocessing techniques. In accordance with an embodiment, the event maycorrespond to a meeting schedule. In accordance with other embodiments,the event may correspond to a planning a long drive, air travel, diningin a restaurant, staying in a hotel, and the like. The event may bedetermined based on the words, voice fingerprints, acousticcharacteristics, or other parameters determined by the ASR system 318and keywords extracted from the resultant text.

The user recognizer 322 may be configured to recognize voice and/oracoustic fingerprints in the speech signals uttered by a user. The voicefingerprint may correspond to a set of unique characteristics of a sound(or a voice), such as, variance in frequencies, tempo, average zerocrossing rate, average flatness, frequency spectrum, prominent tones,frequency spikes, and the like. The voice fingerprint may be used todistinguish the voice of one user from another user. The user recognizer322 may analyze one or more voices in the speech signals for variouscharacteristics and generate a fingerprint for corresponding voice ofeach user. The speech signals that include the one or more voices may betransformed into a spectrogram, which may be analyzed for the uniquecharacteristics of the one or more voices.

The user recognizer 322 may determine the number of voice fingerprintsin the speech signals and may also determine which voice fingerprint isspeaking a specific word or sentence within the speech signals. Further,the voice fingerprint may be used to identify an identity of the useruttering at least a part of the speech signals. Thus, the voicefingerprint may also be used to authenticate the user.

In accordance with an embodiment, the voice fingerprint template may beformed based on previously gathered audio data associated with the voiceof the user and may include characteristics of the voice and stored inthe user profile database 332. The voice fingerprint template may beupdated or adjusted based on additional audio data associated with thatuser's voice as the audio data is being captured. The user recognizer322 may compare the voice fingerprint found in the speech signals withthe voice fingerprints template stored in the user profile database 332and may determine whether the voice of the user uttering the speechsignal is the speaker of voice fingerprint template.

The consent detection engine 324 may comprise suitable logic, circuitry,and interfaces that may be configured to detect an immediate consent ordissent of the first user 106 a or the second user 106 b at a definedtimestamp during the personal interaction. The immediate consent ordissent of the first user 106 a or the second user 106 b may be based onat least one of the comparison of the confidence score of the speechrecognition and the intent of the first user 106 a and the second user106 b with a threshold value, one or more explicit or implied keywordsfrom the uttered speech signals, and an extent of deviated values of thebiometric data.

In accordance with an embodiment, the confidence score may be determinedbased on an intent of both of the first user 106 a and the second user106 b, current sensor data, and a new set of user characteristicspredicted for the first user 106 a and the second user 106 b during thepersonal interaction. The confidence score may indicate a level ofconfidence that the detected immediate consent response from the firstuser 106 a or the second user 106 b is affirmative or negative. In casethe confidence score exceeds the threshold value, it is implied that thefirst user 106 a or the second user 106 b is consenting for one or moreactivities performed during the personal interaction. On the contrary,if the confidence score does not exceed the threshold value, it isimplied that the first user 106 a or the second user 106 b is notconsenting for one or more activities performed during the personalinteraction. Accordingly, the consent detection engine 324 may detectthe immediate consent or dissent of the first user 106 a or the seconduser 106 b at a defined timestamp during the personal interaction.

In accordance with an embodiment, the consent detection engine 324, inconjunction with the ML engine 314, may determine the confidence scorebased on a statistical classification technique. Examples of thestatistical classification technique may include, but are not limitedto, a maximum-entropy classifier, a logistic regression classifier, aNaive Bayes classifier, and a support vector machine.

The consent validation engine 326 may comprise suitable logic,circuitry, and interfaces that may be configured to validate thedetected immediate consent or dissent of the first user 106 a or seconduser 106 b based on a plurality of criteria. The plurality of criteriafor the validation of the detected immediate consent or dissent of thefirst user 106 a or the second user 106 b may comprise at least aninformed, freely given, reversible, enthusiastic, and specific agreementbetween the first user 106 a and the second user 106 b to perform one ormore activities during the personal interaction. The In accordance withan embodiment, the validation of the immediate consent or dissent may beevaluated based on a weighted scoring method. The method may includecomputing a plurality of weights using training data and machinelearning techniques by the ML engine 314. The plurality of criteria maybe weighted, where the weight signifies the absolute/relativesignificance of the criterion. In accordance with an embodiment, theweights may be adjusted automatically, using known methods of supervisedtraining. Accordingly, a weighted combination (for example a weightedaverage) may be computed that may indicate a validation score.

The task manager 328 may be configured to generate one or more tasks andexecute various application programs and services based on thedetermined event. The event may be determined based on the words, voicefingerprints, acoustic properties, negation keywords, or otherparameters determined by ASR system 318 and the NLP engine 320. Forexample, the NLP engine 320 may determine that the users intend to catchup and dine together based on the voice fingerprints, such as “Cool,then we should meet tomorrow for dinner and chat more.” Accordingly, thetask manager 328 may generate a first set of tasks, for example, fixinga meeting schedule in the electronic calendars associated with each ofthe participating users, book a nearby restaurant for dinner the nextday, and generate reminders for both the users one hour prior to thescheduled time.

In accordance with an embodiment, the determined event may correspond toa dissent of the first user 106 a or the second user 106 b. In suchcase, the task manager 328 may be configured to perform a one or moretasks from the second set of tasks based on the validation of thedetected dissent of the first user 106 a or the second user 106 b forperforming an activity during the personal interaction in the real time.Non-limiting examples of such tasks from the second set of tasks mayinclude, dialing an emergency number of current location, dialing anumber of an emergency contact person, or activating an emergency alarmsound.

In accordance with an embodiment, upon a user request of the first user106 a or the second user 106 b, the task manager 328 may be configuredto publish a detailed report of the plurality of messages exchanged bythe two users and time sequence-based recording of conversation incurredduring the personal interaction. The detailed report may be generatedbased on the output of the CMS 110, such as validation of a detecteddissent of the first user 106 a or the second user 106 b. The taskmanager 328 may be configured to determine additional informationassociated with the published report. Examples of the additionalinformation determined may include, but are not limited to, number ofinteracting users, the identities the interacting users, type of theinteraction (i.e., whether the interaction is a seminar, lecture,business discussion, interview, or a personal interaction), the mood ofthe users during the interaction (i.e., monotonous, exciting, angry,seductive, highly stimulating, sad, or coercive), the duration of theinteraction, the date and time of the interaction, and the like. Theadditional information may be determined based on the plurality ofmessages, uttered speech signals, words, voice fingerprints, acousticproperties, or other parameters.

The detailed report may include keywords, key sentences, paraphrasedsentences of main pointers, bullet-point phrases, and the like,pertaining to the personal interaction of the first user 106 a or thesecond user 106 b. One portion, i.e., the summary portion, of thedetailed report may provide a brief account of the personal interaction,which may enable a reader to understand the context, main pointers,and/or significant events during the personal interaction of the firstuser 106 a or the second user 106 b. In some scenarios, sentences fromthe personal interaction may be double quoted and then paraphrased, ornew sentences may be generated, to include or provide context tokeywords. The keywords may be identified based on whether it is spokenby a key user or based on acoustic properties or other parametersassociated with the speech session.

The alert engine 330 may comprise suitable logic, circuitry, andinterfaces that may be configured to generate an alert related tovarious senses, such as visual, auditory, tactile senses, and the like,for the exemplary electronic device 202. The generated alerts may betransmitted by the second network interface 302 to the first networkinterface 204 of the exemplary electronic device 202. Various examplesof the alert may include a display alert, a sound alert, a haptic alert,an optical alert, and the like.

In accordance with an embodiment, the alert engine 330 may render suchalerts, for example, display alert, a sound alert, a haptic alert, anoptical alert, and the like, at various units of the exemplaryelectronic device 202. For example, the display alert, in form of alertmessages, may be displayed at the user interface of the applicationprogram 112 presented on the display unit 220 a of the exemplaryelectronic device 202. The sound alert, in form of buzzers or beeps, maybe reproduced by the sound output unit 220 b of the exemplary electronicdevice 202. The haptic alert, in form of various tactile effects, forexample a vibration, may be reproduced by the haptic unit 220 c. Theoptical alert, in form of flashlight, may be reproduced by the opticalunit 220 d of the exemplary electronic device 202. Such alerts may bereproduced at the exemplary electronic device 202 while, for example,receiving a message, receiving a call signal, a missed call, an alarm, aschedule notification, receiving an email, receiving information throughthe application program 112, receiving a consent response, execution ofthe first and the second set of tasks, and the like.

The user profile database 332 may store multiple user profiles for theplurality of users 106 registered with the CMS 110. The user profile maystore information about each user from the plurality of users 106, forexample, a name, an age, a location, a family size, an income, a workindicator, a preference of living environment, a browsing history,biometric data, social media data (i.e., a plurality of media shared,liked or tagged, content posts, tweets, and information relating to thesocial media contacts), one or more deep learning factors derived frompictures or visual descriptions, or any combination thereof. The userprofile database 332 may further store current set of usercharacteristics identified based on time sequence-based userinteractions and the uttered speech signals of each user during thepersonal interaction. Examples of such current user characteristics mayinclude current state of, for example, geographical origin, behaviour,state of mind, mental health, physical health, level of intoxication,moods and emotions, sleepiness and fatigue, personality traits (forexample, sociability, activity, aggression-hostility, impulsivesensation seeking, and neuroticism-anxiety), and the like. The userprofile database 332 may further store a new set of user characteristicspredicted for the users during corresponding personal interactions. Thenew set of user characteristics may be predicted based on timesequence-based personal interaction data, training data set, andartificial neural network. Examples of the new set of usercharacteristics may include predicted state of, for example, behaviour,state of mind, mental health, physical health, level of intoxication,moods and emotions, sleepiness and fatigue, personality traits (forexample, sociability, activity, aggression-hostility, impulsivesensation seeking, and neuroticism-anxiety), and the like.

The task list database 334 may store data about a list of actionableitems, operations or functions that may be performed by the CMS 110 inresponse to the validation of consent or dissent of a user during thephysical interaction based on the uttered speech signals. The task listdatabase 334 may sore all the possible first and the second set oftasks. For example, an actionable item, operation, or function maycorrespond to creating an event or appointment on electronic calendarsof both the first user 106 a and the second user 106 b when the both thefirst user 106 a and the second user 106 b agree for a physicalinteraction. In other examples, the actionable item, operation, orfunction may correspond to one of dialing an emergency number of currentlocation, dialing a number of an emergency contact person, or activatingan emergency alarm sound, upon the validation of dissent detected aboutthe second user 106 b. In yet another example, an actionable item,operation, or function may correspond to publishing, based on a userrequest by the first user or the second user, a time sequence-basedrecording of conversation incurred during the personal interaction. Inaccordance with an embodiment, the data corresponding to the first andthe second set of tasks may include various metadata, such as date,timelines, a submission method, topic, subject, notes, personnelresponsible, and the like.

The consent database 336 may store a plurality of records pertaining toan indicia of a plurality of consent requests, consent responses, and achain of consent negotiations and associated metadata. Each record fromthe plurality of records may provide an indication of a plurality ofaspects, for example, the user who requested consent, a user whoresponded to the request, a sequence of consent requests and responsesthat occur during consent negotiation between two users, type ofresponse (for example, acceptance or rejection to the consent request),the date/time of the consent request and the consent response generated,and any task that was executed based on an immediate consent or dissentof a user. Each record may be securely stored in the consent database336 based on one or more encryption techniques, for example,Transparent/External database encryption, Column-level encryption,Field-level encryption, Filesystem-level encryption, Full diskencryption, Symmetric and asymmetric database encryption, andApplication-level encryption.

The additional database 338 may store training data sets (from existingfiles) that are used by one or more training modules in the ML engine314 to train various models, such as the acoustic model 344, thelanguage model 350, or other such transcription models. The trainingmodules may include machine learning algorithms, such as, but notlimited to, deep learning neural networks, artificial neural networks,various statistical classifiers, gradient boosting, random forests,support vector machine learning, decision trees, variationalauto-encoders (VAE), and generative adversarial networks.

The additional database 338 may be periodically updated with data fromrecently run models via an accumulator (not shown). In accordance withan embodiment, the training data set may not have a correspondingtranscript. In such case, a human transcription may be obtained to serveas the ground truth that may refer to the accuracy of the classificationof the training data set. In accordance with an embodiment, the trainingmodule may train a transcription model using previously generatedtraining data sets. In accordance with an embodiment, the trainingmodule may train a transcription model using both existing historicaltraining data and the most recent transcribed data.

It may be noted that the user profile database 332, the task listdatabase 334, the consent database 336, and the additional database 338may be implemented using various types of data storage technologies andstandards, for example, ROM, RAM, DRAM, SRAM, SDRAM, magneticrandom-access memory (MRAM), solid state, two and three-dimensionalmemories, Flash®, and other such memory devices.

It should be noted that all or parts of hardware components of thevarious sub-systems of the CMS 110 disclosed herein may readily beprovided in a system-on-a-chip (SoC), including a central processingunit (CPU) package. In accordance with an embodiment, the SoC maycorrespond to an integrated circuit (IC) that integrates components of acomputer or other electronic system into a single chip. The SoC maycontain digital, analog, mixed-signal, and radio frequency functions,all of which may be provided on a single chip substrate. Otherembodiments may include a multi-chip-module (MCM), with multiple chipslocated within a single electronic package and configured to interactclosely with each other through the electronic package. In accordancewith another embodiment, the computing functionalities of the CMS 110disclosed herein may be implemented in one or more silicon cores in aRISC processor, an ASIC processor, a CISC processor, FPGAs, and othersemiconductor chips, processors, or control circuits.

It should be noted that the terms “processor” or “microprocessor”include not only a traditional microprocessor (such as Intel's®industry-leading x86 and x64 architectures), but also graphicsprocessors, matrix processors, and any ASIC, FPGA, microcontroller, DSP,programmable logic device, PLA, microcode, instruction set, emulated orvirtual machine processor, or any similar device, combination ofdevices, or logic elements (hardware or software) that permit theexecution of instructions.

In operation, a secure communication channel may be established as theapplication program 112 is downloaded and installed at the plurality ofelectronic devices 104. The secure communication channel may beestablished between each instance of the first network interface 204 ofthe exemplary electronic device 202, such as the first electronic device104 a and the second electronic device 104 b, and the second networkinterface 302 of the CMS 110. The secure communication channel may berealized within the wide area network 118 b using a plurality of securecommunications protocols. Examples of the secure communicationsprotocols may include, but are not limited to, Secure Socket Layer(SSL), Transport Layer Security (TLS), Swipe IP Security Protocol(SWIPE), Secure Remote Procedure Call (S-RPC), Challenge HandshakeAuthentication Protocol (CHAP), Extensible Authentication Protocol(EAP), and the like.

Once the secure communication channel is established, the secondprocessor 312 in conjunction with the second network interface 302 ofthe CMS 110 may receive detailed information, pertaining to theplurality of users 106, from the plurality of electronic devices 104,via the secure communication channel. The detailed information may becaptured at the plurality of electronic devices 104 through the userinterface of the application program 112 presented at each of theplurality of electronic devices 104 during registration.

Based on the detailed information, pertaining to the plurality of users106, received from the plurality of electronic devices 104, the secondprocessor 312 in conjunction with the ML engine 314, and the AI engine316 of the CMS 110, may generate a plurality of user profiles. Each ofthe plurality of user profiles includes a name, an age, a location, afamily size, an income, a work indicator, a preference of livingenvironment, a browsing history, one or more deep learning factorsderived from pictures or visual descriptions, or any combinationthereof. The second processor 312 of the CMS 110 may store the pluralityof user profiles in the user profile database 332.

In accordance with an embodiment, the first user 106 a may select asecond user profile of the second user 106 b, via the user interface ofthe application program 112 presented at the first electronic device 104a. For the selection of the second user profile of the second user 106 bby the first user 106 a, the second processor 312 in conjunction withthe ML engine 314 and the AI engine 316 of the CMS 110, may recommendthe second user profile of the second user 106 b to the first user 106 avia the user interface of the application program 112 presented at thefirst electronic device 104 a. The second user profile of the seconduser 106 b may be recommended based on a plurality of options presentedby the user interface of the application program 112. Accordingly, thesecond profile of the second user 106 b may be selected by the firstuser 106 a based on user preferences of the first user 106 a. Inaccordance with another embodiment, the second processor 312 of the CMS110, may search the second user profile of the second user 106 b fromthe plurality of user profiles based on one or more search termsprovided by the first user 106 a at the user interface of theapplication program 112 presented at the first electronic device 104 a.In accordance with another embodiment, the second processor 312 inconjunction with the ML engine 314 and the AI engine 316 of the CMS 110,may generate rating factors for other users from the plurality of users106 based on one or more machine learning models. The rating factor maycomprise a probability of a user interaction of the first user 106 awith another user from the plurality of users 106. The second processor312 in conjunction with the ML engine 314 and the AI engine 316 of theCMS 110, may recommend the second user profile of the second user 106 bbased on ranking of the rating factors for the other users from theplurality of users 106.

In accordance with an embodiment, the second processor 312 of the CMS110 may enable the first electronic device 104 a and the secondelectronic device 104 b to exchange a plurality of messages prior to thepersonal interaction between the first user 106 a and the second user106 b. Each of the plurality of messages may comprise identificationinformation associated with the first user 106 a and the second user 106b, and a payload comprising a plurality of text-based messages,voice-based messages, and video messages.

In accordance with an embodiment, the second processor 312 of the CMS110 may receive, via the secure communication channel, a consent requestfrom the first electronic device 104 a associated with the first user106 a based on a selection of the second user profile of the second user106 b by the first user 106 a. The consent request may correspond to amutual agreement to perform one or more activities during the personalinteraction between the first user 106 a and the second user 106 b. Thesecond processor 312 of the CMS 110 may transmit, via the securecommunication channel, the consent request to the second electronicdevice 104 b. The second processor 312 of the CMS 110 may receive, viathe secure communication channel, the consent response from the secondelectronic device 104 b based on options selected or data provided bythe second user 106 b on the consent request generated by the first user106 a. In accordance with an embodiment, the second processor 312 of theCMS 110 may facilitate multiple rounds of consent request and responses,referred to as a consent negotiation, between the first user 106 a andthe second user 106 b until accepted by both of the first user 106 a andthe second user 106 b on a set of terms, conditions, and clausesacceptable to both.

In accordance with an embodiment, the CMS 110 may determine an eventbased on analysis of the plurality of messages using natural languageprocessing techniques. Corresponding to the event, a first set of tasksmay be generated for the personal interaction between the first user 106a and the second user 106 b. The first set of tasks generatedcorresponding to the event may comprise at least generating a calendarentry for both of the first user 106 a and the second user 106 b forscheduling the personal interaction and booking a meeting venue for thepersonal interaction.

In accordance with an embodiment, the CMS 110 may enable the first user106 a and the second user 106 b for a direct exchange of one or moreidentity confirmation messages via corresponding user interfaces of anapplication program presented at the first electronic device 104 a andthe second electronic device 104 b respectively, during or prior to thepersonal interaction between the first user 106 a and the second user106 b.

The CMS 110 may receive, during a personal interaction between the firstuser 106 a and the second user 106 b, uttered speech signals and sensordata of at least the first user 106 a from at least the first electronicdevice 104 a, via the secure communication channel. The personalinteraction may be scheduled based on the acceptance of the consentrequest by the first electronic device 104 a or the second electronicdevice 104 b. In accordance with an embodiment, the uttered speechsignals may be extracted from a conversation between the first user 106a and the second user 106 b during the personal interaction.

The CMS 110 may identify a current set of user characteristics based ontime sequence-based user interactions and the uttered speech signals ofeach user during the personal interaction. The current set of usercharacteristics may be utilized as a training data set for theprediction of the new set of user characteristics. The CMS 110 maypredict a new set of user characteristics for the first user 106 a andthe second user 106 b during the personal interaction based on timesequence-based personal interaction, the training data set, and anartificial neural network model. In accordance with an embodiment, theprediction of the new set of user characteristics may be further basedon social media data of the first user 106 a and the second user 106 bcollected from one or more public information databases. The socialmedia data may include a plurality of media shared, content posts,social media contacts having a predefined social media distance betweenuser accounts and information relating to the social media contacts.

The CMS 110 may determine a confidence score based on an intent of bothof the first user 106 a and the second user 106 b, current sensor dataand the new set of user characteristics predicted for the first user 106a and the second user 106 b during the personal interaction. The CMS 110may further detect an immediate consent or an immediate dissent of oneof the first user 106 a or the second user 106 b at a defined timestampduring the personal interaction based on at least one of a comparison ofthe confidence score with a threshold value, one or more explicit orimplied keywords from the uttered speech signals, and an extent ofdeviated values of the sensor data. The CMS 110 may further validate theimmediate consent or the immediate dissent of one of the first user 106a or the second user 106 b based on a plurality of criteria. Inaccordance with an embodiment, the plurality of criteria for thevalidation of the immediate consent or dissent of the first user 106 aor the second user 106 b may comprise at least informed, freely given,reversible, enthusiastic, and specific agreement between the first user106 a and the second user 106 b to perform one or more activities duringthe personal interaction.

In accordance with an embodiment, the CMS 110 may override theacceptance on the consent request by the immediate dissent one of thefirst user 106 a or the second user 106 b for performing an activityduring the personal interaction. The CMS 110 may perform a second set oftasks based on the validation of the immediate dissent of one of thefirst user 106 a or the second user 106 b. In accordance with anembodiment, the CMS 110 may publish, based on a user request, a timesequence-based recording of conversation incurred during the personalinteraction. The published time sequence-based recording of theconversation incurred during the personal interaction may correspond toone of the second set of tasks. Further, other tasks from the second setof tasks performed based on the validation of the immediate dissent ofone of the first user 106 a or the second user 106 b may comprisedialing an emergency number of current location, dialing a number of anemergency contact person, or activating an emergency alarm sound.

FIGS. 4A, 4B, and 4C depict flowcharts 400A, 400B, and 400Crespectively, that collectively illustrate exemplary operations forconsent detection and validation, in accordance with various embodimentsof the disclosure. FIGS. 4A, 4B, and 4C are described in conjunctionwith FIGS. 1, 2A to 2C, 3A, 3B, and 5A to 5D.

At 402, a secure communication channel with the first electronic device104 a and the second electronic device 104 b may be established. Inaccordance with an embodiment, the second processor 312 in the CMS 110may be configured to establish the secure communication channel betweenthe second network interface 302 of the CMS 110 and the first networkinterface 204 of both of the first electronic device 104 a and thesecond electronic device 104 b based on one or more securecommunications protocols, for example, SSL, TLS, SWIPE, Secure S-RPC,CHAP, EAP, and the like.

In accordance with an embodiment, the second processor 312 in the CMS110 may be configured to establish the secure communication channel withthe first electronic device 104 a and the second electronic device 104 bbased on, for example, a web-server operating Hyper Text TransferProtocol Secure (HTTPS) protocol or a virtual private network (VPN)connection. Such a secure communication channel may guarantee theconfidentiality and integrity of the data communicated between the CMS110 and the plurality of electronic devices 104, such as the firstelectronic device 104 a and the second electronic device 104 b.

In accordance with an embodiment, the secure communication channel maybe established when the plurality of electronic devices 104, such as thefirst electronic device 104 a and the second electronic device 104 b,may download and install the application program 112, referred to as an“app”, that facilitates a variety of functionalities for the associatedplurality of users 106. Examples of such functionalities may include,but are not limited to, enabling the first user 106 a to search the userprofile of the second user 106 b, recommending the user profile of thesecond user 106 b to the first user 106 a, various modes of electroniccommunication between the first user 106 a and the second user 106 b(such as managing consent requests, exchanging text messages,facilitating personal email, phone calls, video calls, and the like),various tasks (such as recording, processing, analyzing, and publishingthe conversation between the first user 106 a and the second user 106 bduring a personal interaction), generating alerts in response todetection of an immediate dissent of the first user 106 a or the seconduser 106 b, and the like.

Once the secure communication channel is established, the applicationprogram 112 may synchronize the CMS 110 with the plurality of electronicdevices 104, such as the first electronic device 104 a and the secondelectronic device 104 b. Accordingly, the second processor 312 of theCMS 110 may automatically configure the settings of the plurality ofelectronic devices 104, such as the first electronic device 104 a andthe second electronic device 104 b. The second processor 312 of the CMS110 may also enable an administrator to control the functionality of theplurality of electronic devices 104. Also, during synchronization, datacollected, generated, and stored by the plurality of electronic devices104 may be received by the second processor 312 in conjunction with thesecond network interface 302 of the CMS 110, via the securecommunication channel, and may be later on accessed by theadministrator.

At 404, detailed information, pertaining to the plurality of users 106,may be received from the plurality of electronic devices 104, via thesecure communication channel. In accordance with an embodiment, thesecond processor 312 in conjunction with the second network interface302 of the CMS 110, may be configured to receive the detailedinformation, pertaining to the plurality of users 106, from theplurality of electronic devices 104, via the secure communicationchannel. The detailed information, pertaining to the plurality of users106, may be captured at the plurality of electronic devices 104 throughthe user interface of the application program 112 presented at each ofthe plurality of electronic devices 104 during registration. Inaccordance with an embodiment, the detailed information may be receivedcryptographically by the second network interface 302, via the wide areanetwork 118 b, for security purposes.

Prior to the receipt of the detailed information, each of the pluralityof users 106 may be required to be registered with the CMS 110 using theapplication program 112, via the secure communication channel of thewide area network 118 b. The plurality of users 106 may be required tobe registered with the CMS 110 once the application program 112 isdownloaded and installed at each of the plurality of electronic devices104. During the registration, also referred to as the sign-up process oruser/device enrolment, a plurality of editable and selectable optionsand/or questionnaire may be presented at the display unit 220 a of eachof the plurality of electronic devices 104, via the user interface ofthe application program 112. For registration, each of the plurality ofusers 106 may operate one or more associated devices, each correspondingto the input unit 218, for example, a keyboard, a touch key, mouse, andthe like, in the exemplary electronic device 202, such as the firstelectronic device 104 a and the second electronic device 104 b. Theinput unit 218 may be operated by an associated user to manipulate theplurality of editable and selectable options and/or answer thequestionnaire to provide the detailed information. For example, the userinterface of the application program 112 presented at the firstelectronic device 104 a may receive the detailed information pertainingto the first user 106 a. Similarly, the user interface of theapplication program 112 presented at the second electronic device 104 bmay receive the detailed information pertaining to the second user 106b. The detailed information may include name, age, a location, a familysize, an income, a work indicator, a preference of living environment, abrowsing history, and the like, pertaining to the corresponding user.

In accordance with an embodiment, the detailed information may furtherinclude biometric data pertaining to each of the plurality of users 106during the registration. In such an embodiment, the sensing unit 216 inthe exemplary electronic device 202, such as the first electronic device104 a and the second electronic device 104 b, may be configured todetect information about an associated user, such as the first user 106a and the second user 106 b, respectively. The first processor 214 maybe configured to digitize the information according to asoftware-defined template and use the same template for comparison andunique identification/recognition of the user for biometricauthentication. New-age security systems do not hesitate to rely solelyon such biometric authentication, eliminating the need for the user toremember passwords or carry security tokens. When a user requestsaccess, a new sample of biometric data may be collected by the sensingunit 216 in the exemplary electronic device 202. In an embodiment, thefirst processor 214 may be configured to compare the new sample ofbiometric data with the stored templates to determine if the user isauthorized or not. In accordance with another embodiment, the firstprocessor 214, in conjunction with the first network interface 204, maybe configured to transmit the new sample of biometric data to the CMS110. In such case, the second network interface 302 may be configured toreceive the new sample of the biometric data of the user and the secondprocessor 312 may be configured to compare the new sample of biometricdata with the stored templates from the user profile database 332.Matching with the stored templates determines whether the user identityis authorized or not.

As described above, the biometric data may include one or more of themorphological biometric data, bio-signals, behavioral biometric data,and biological biometric data corresponding to the associated user.Examples of the biometric data may include, but not limited to, voiceidentification, retinal identification, facial identification,fingerprint identification, electroencephalogram-based identification,hand expressions, facial expressions, vocal expressions, body gestures,eye tracking, and the like. Primarily, a set of such biometric data,such as the morphological biometric data, may be used for devicesecurity and user authentication. Other biometric data, such as thebio-signals, the behavioral biometric data, and the biological biometricdata, may be detected in real time during the later operational steps ofthe CMS 110.

At 406, a plurality of user profiles may be generated based on thedetailed information, pertaining to the plurality of users 106, receivedfrom the plurality of electronic devices 104. In accordance with anembodiment, the second processor 312 in conjunction with the ML engine314, the AI engine 316, and the NLP engine 320 of the CMS 110, may beconfigured to generate the plurality of user profiles based on thedetailed information, pertaining to the plurality of users 106, receivedfrom the plurality of electronic devices 104. The user profile mayinclude the name, age, location, a family size, an income, a workindicator, a preference of living environment, educational background, abrowsing history, biometric data, and the like.

In accordance with an embodiment, the ML engine 314 and the AI engine316 may be configured to derive one or more deep learning factors frompictures or visual descriptions, or any combination thereof for the userprofile. In accordance with an embodiment, the NLP engine 320 may beconfigured to search for unstructured data and perform NLP analysis ofdata obtained from, for example, social network sites where the user isidentified as a subject of a conversation for the user profile.

Accordingly, the generated user profile may include a variety ofinformation, such as a first type of information provided directly bythe user, a second type of information derived or inferred from the dataassociated with the user, and a third type of information determinedbased on NLP analysis of the unstructured data associated with the user.The aggregation of the first type of information, the second type ofinformation, and the third type of information may be used to generatethe user profile of each of the plurality of users 106.

At 408, the plurality of user profiles may be stored in the user profiledatabase 332. In accordance with an embodiment, the second processor 312of the CMS 110 may be configured to store the plurality of user profilesin the user profile database 332. The user profile database 332 may beimplemented with any desirable database architecture known in the art,such as a relational database, an object-oriented database, and/or oneor more tables. In accordance with various embodiments, the user profiledatabase 332 may be hosted by the CMS 110 and/or on one or more remotecomputing systems accessible via the wide area network 118 b.

Once the plurality of user profiles is stored in the user profiledatabase 332, the first user 106 a may be enabled to select the seconduser profile of the second user 106 b, via the user interface of theapplication program 112 presented at the first electronic device 104 a.In accordance with an embodiment, the second processor 312 inconjunction with the ML engine 314 and the AI engine 316 of the CMS 110,may be configured to perform the selection of the second user profile ofthe second user 106 b for the first user 106 a. Accordingly, the seconduser profile of the second user 106 b may be recommended by therecommendation engine 316 b of the AI engine 316 to the first user 106a, via the user interface presented at the first electronic device 104a. Based on the recommendation, the second user profile of the seconduser 106 b may selected for the first user 106 a.

In various embodiments, a plurality of options and/or a guidedquestionnaire may be presented by the user interface of the applicationprogram 112 at the first electronic device 104 a of the first user 106 ain order to obtain the user preferences of the first user 106 a. Thefirst user 106 a may provide the user preferences by selecting one ormore of the plurality of options and suitably responding to the guidedquestionnaire based on the user preferences of the first user 106 a.Such user preferences of the first user 106 a may be matched with theuser profiles of other users from the plurality of users 106 in the userprofile database 332. Accordingly, the user profile database 332 may besearched for the user profiles that match the user preferences of thefirst user 106 a, and the second user profile of the second user 106 bmay be outputted from the user profile database 332 that matches theuser preferences of the first user 106 a within a given threshold. Invarious embodiments, multiple user profiles may be ranked by how closelythey match the user preferences of the first user 106 a. In this manner,the second processor 312 in conjunction with the ML engine 314 and theAI engine 316 of the CMS 110, may enable the first user 106 a to findanother user (i.e., the second user 106 b) with whom the first user 106a may be most compatible with and with whom the first user 106 a is mostlikely to otherwise have satisfying personal (or professional)relationship.

In accordance with another embodiment, the second processor 312 of theCMS 110 may be configured to search the user profile of the second user106 b based on one or more terms provided by the first user 106 a at theuser interface of the application program 112 presented at the firstelectronic device 104 a. The first user 106 a may provide one or moresearch terms, such as name, age, location, one or more user preferencesin the search tab provided at the user interface of the applicationprogram 112. Such search terms may be matched by the second processor312 with the user profiles of other users from the plurality of users106. Accordingly, the second processor 312 may search the user profiledatabase 332 for the user profiles that match the user preferences ofthe first user 106 a and output the second user profile of the seconduser 106 b from the user profile database 332 that closely matches theuser preferences of the first user 106 a.

In accordance with another embodiment, the second processor 312 inconjunction with the ML engine 314 and the AI engine of the CMS 110, maybe configured to identify the user profiles and time sequence of userinteractions of the first user 106 a and other users from the pluralityof users 106. The user profiles of the first user 106 a and the otherusers from the plurality of users 106 may include the historical dataretrieved from the user profile database 332. The time sequence of userinteractions may include clicking on a display element on socialnetworking sites and other websites, commenting on a post, tagging apost, sharing a post, liking a post, or any combination thereof, by eachof the first user 106 a and other users from the plurality of users 106.

In accordance with an embodiment, the second processor 312 inconjunction with the ML engine 314 and the AI engine 316, may beconfigured to generate rating factors for other users from the pluralityof users 106 based on one or more machine learning models retrieved fromthe additional database 338. The rating factor may comprise aprobability of a user interaction of the first user 106 a with each ofthe other users from the plurality of users 106. The one or more machinelearning models may include, for example, multi-Layer perceptron,Adaboost, Logistic Regression, Gradient Boosting Tree, Reverse BoltzmanMachine, Random Forest, K Nearest Neighbor models, and/or the like.Based on ranking of the generated rating factor for the other users fromthe plurality of users 106, the second processor 312 in conjunction withthe ML engine 314 and the AI engine 316, may be configured to recommendthe second user profile of the second user 106 b to the first user 106a. In accordance with an embodiment, the ML engine 314 and the AI engine316 may be configured to monitor the user activity of the first user 106a and use the data to improve the machine learning model over time. Forexample, the ML engine 314 and the AI engine 316 may determine whetherthe second user 106 b is preferable to the first user 106 a. If thefirst user 106 a chooses to select the second user profile of the seconduser 106 b, the interest of the first user 106 a may be collected as adata point and added to a set of training data. The previous machinelearning model may be loaded from a saved state and updated according tothe augmented training data. In certain examples, a threshold amount ofdata points may be collected prior to generating the machine learningmodel.

At 410, an exchange of a plurality of messages between the firstelectronic device 104 a and the second electronic device 104 b may beenabled prior to the personal interaction between the first user 106 aand the second user 106 b. In accordance with an embodiment, the secondprocessor 312 of the CMS 110 may be configured to enable the exchange ofthe plurality of messages between the first electronic device 104 a andthe second electronic device 104 b prior to the personal interactionbetween the first user 106 a and the second user 106 b. Each of theplurality of messages may comprise identification information associatedwith the first user 106 a and the second user 106 b, and a payloadcomprising a plurality of text-based messages, voice-based messages, andvideo messages.

Via the secure communication channel established between the CMS 110 andthe plurality of electronic devices 104, such as the first electronicdevice 104 a and the second electronic device 104 b, the first user 106a and the second user 106 b may securely communicate with each otherthrough chats, text messages, video calls, or voice calls. The pluralityof messages may be exchanged between the first electronic device 104 aand the second electronic device 104 b, via the secured communicationchannel, prior to the personal interaction between the first user 106 aand the second user 106 b. Each of the plurality of messages maycomprise identification information associated with the first user 106 aand the second user 106 b, and a payload comprising a plurality oftext-based messages, voice-based messages, and video messages. Once thevirtual relationship between the first user 106 a and the second user106 b progresses, one of the first user 106 a or the second user 106 bmay initiate to schedule an in-person meeting or a personal interaction.

At 412, a consent request may be received from the first electronicdevice 104 a addressed for the second user 106 b via the securecommunication channel based on a selection of the second user profile ofthe second user 106 b. In accordance with an embodiment, the secondprocessor 312 in conjunction with the second network interface 302 ofthe CMS 110, may be configured to receive the consent request from thefirst electronic device 104 a addressed for the second user 106 b basedon a selection of the second user profile of the second user 106 b. Theconsent request may be received from the first electronic device 104 avia the secure communication channel.

Based on a selection of the second user profile of the second user 106b, the first processor 214 may be configured to generate the consentrequest addressed for the second user 106 b. The consent request may begenerated at the first electronic device 104 a based on a manipulationof a selectable option, such as a software button presented on the userinterface of the application program 112 at the first electronic device104 a, by the first user 106 a. The manipulation of the selectableoption may present a page for accessing and editing a set of sub-optionsfor creating a mutual agreement. The mutual agreement may pertain toperforming one or more activities during a personal interaction betweenthe first user 106 a and the second user 106 b. In an exemplaryscenario, the mutual agreement may correspond to performing activitieswith set boundaries, such as kissing and touching only, during a sexualinteraction. In another example, the mutual agreement may correspond tointimately engaging in a specific sexual activity in addition to kissingand touching, during the sexual interaction. In yet another example, themutual agreement may correspond to intimately engaging in several typesof sexual activities, during the sexual interaction.

The consent request, thus generated at the first electronic device 104 aby the first user 106 a, may be received by the second processor 312 inconjunction with the second network interface 302, via the wide areanetwork 118 b. The date and time of receipt of the consent request alongwith other details, such as a unique sender and receiver deviceidentifiers and a location of the first electronic device 104 a, may bestored in the consent database 336 for future references. In accordancewith an embodiment, the first user 106 a may withdraw the consentrequest anytime once the consent request (addressed for the second user106 b) is generated at the first electronic device 104 a.

At 414, the consent request may be transmitted to the second electronicdevice 104 b, via the secure communication channel. In accordance withan embodiment, the second processor 312 in conjunction with the secondnetwork interface 302 may be configured to transmit the consent requestto the second electronic device 104 b, via the secure communicationchannel of the wide area network 118 b. In accordance with anembodiment, a notification regarding the transmittance of the consentrequest to the second electronic device 104 b may be sent back to thefirst electronic device 104 a. The date and time of the receipt from thefirst electronic device 104 a and the transmittance of the consentrequest to the second electronic device 104 b along with other details,such as the unique sender and receiver device identifiers and thelocation of the first electronic device 104 a, may be stored in theconsent database 336 for future references.

At 416, a consent response may be received, via the secure communicationchannel, from the second electronic device 104 b based on optionsselected or data provided by the second user 106 b in response to theconsent request generated by first user 106 a. In accordance with anembodiment, the second processor 312 in conjunction with the secondnetwork interface 302 of the CMS 110 may be configured to receive theconsent response from the second electronic device 104 b, via the securecommunication channel, based on options selected or data provided by thesecond user 106 b in response to the consent request generated by firstuser 106 a. In accordance with an embodiment, the second user 106 b maysimply accept or reject the consent request by manipulating selectableoptions, such as a yes/accept or no/reject/deny software buttons,presented at the user interface of the application program 112 presentedat the second electronic device 104 b. In accordance with an embodiment,the second user 106 b may negotiate and revise the consent request byediting the terms, conditions, and/or clauses of the mutual agreement inthe received consent request. In such an embodiment, the revised consentrequest may be transmitted back to the CMS 110, via the wide areanetwork 118 b.

At 418, the consent response may be evaluated. In accordance with anembodiment, the second processor 312 in the CMS 110 may be configured toevaluate the consent response. In an embodiment, the consent responsemay one of an acceptance, a rejection, or a negotiation by the seconduser 106 b on the consent request generated by the first user 106 a.

In accordance with an embodiment, the consent response is a rejection bythe second user 106 b on the consent request generated by the first user106 a. In such an embodiment, the flowchart 400A terminates.Accordingly, the second processor 312 of the CMS 110 may be configuredto send a rejection notification to the first electronic device 104 aand the second electronic device 104 b, via the wide area network 118 b.

In accordance with another embodiment, the consent response is anacceptance by the second user 106 b on the consent request generated bythe first user 106 a. In such an embodiment, the control passes to step422 in flowchart 400B.

In accordance with yet another embodiment, the consent response is anegotiation (by the second user 106 b) on the consent request generatedby the first user 106 a. In such an embodiment, the control passes tostep 420.

At 420, negotiation between the first user 106 a and the second user 106b may be facilitated, via the secure communication channel. Inaccordance with an embodiment, the second processor 312 in the CMS 110may be configured to facilitate the negotiate between the first user 106a and the second user 106 b, via the secure communication channel. Insuch an embodiment, the consent request may be iteratively revised byone of the first user 106 a and the second user 106 b until accepted byboth of the first user 106 a and the second user 106 b on a set ofterms, conditions, and clauses acceptable to both. For example, uponreceiving the consent request from the CMS 110 via the securecommunication channel of the wide area network 118 b, the second user106 b may revise the consent request by editing the terms, conditions,and/or clauses of the mutual agreement in the received consent request.The revised consent request may be transmitted back to the firstelectronic device 104 a, via the secure communication channel of thewide area network 118 b. In response, the first user 106 a may accept,reject, or counter-revise the revised consent request received from thesecond electronic device 104 b. Such a negotiation may continue untilthe first user 106 a and the second user 106 b reach a common set ofterms, conditions, and clauses acceptable to both. The date and time ofreceipt of the complete chain of negotiations, along with other details,such as unique sender and receiver device identifiers and the locationsof the first electronic device 104 a and the second electronic device104 b, may be duly stored in the consent database 336 for futurereferences. Control passes to step 422 in flowchart 400B.

At 422 of the flowchart 400B in FIG. 4B, an event may be determinedbased on an analysis of the plurality of messages using natural languageprocessing techniques. In accordance with an embodiment, the secondprocessor 312 in conjunction with the NLP engine 320 of the CMS 110, maybe configured to determine the event based on the analysis of theplurality of messages using natural language processing techniques. Forexample, the first user 106 a may post a text message, “day aftertomorrow sounds good for dinner near my office,” for the second user 106b. In response to the above message, the second user 106 b may post avoice message, “catch you then,” for the first user 106 a. The secondprocessor 312 in conjunction with the NLP engine 320 may identify anevent for a possible meeting based on the analysis of the above twomessages using natural language processing techniques.

At 424, a first set of tasks corresponding to the event may be generatedfor the personal interaction between the first user 106 a and the seconduser 106 b. In accordance with an embodiment, the second processor 312in conjunction with the AI engine 316 of the CMS 110, may be configuredto generate a first set of tasks corresponding to the event for thepersonal interaction between the first user 106 a and the second user106 b. For example, based on current date (for example, based on theabove exemplary messages exchanged on Jun. 7, 2022, one task from thefirst set of tasks may be creating a calendar entry dated Jun. 9, 2022,for the electronic calendars of both of the first user 106 a and thesecond user 106 b. Further, another task from the first set of tasks maybe booking a table for dinner at 8 PM in a restaurant at a location inthe vicinity of a current location of the first user 106 a. Furthermore,yet another task from the first set of tasks may be booking a cab forthe first user 106 a from the office location to the restaurant at 7:30PM. The second processor 312 in conjunction with the AI engine 316 andalert engine 330 of the CMS 110, may be configured to presentnotifications at the user interfaces of the application program 112presented at the first electronic device 104 a and/or the secondelectronic device 104 b.

At 426, the first user 106 a and the second user 106 b may be enabledfor a direct exchange of one or more identity confirmation messages viacorresponding user interfaces of the application program 112 presentedat the first electronic device 104 a and the second electronic device104 b, during or prior to a personal interaction between the first user106 a and the second user 106 b. In accordance with an embodiment, thesecond processor 312 in conjunction with the second network interface302 of the CMS 110, may be configured to enable the first user 106 a andthe second user 106 b for a direct exchange of one or more identityconfirmation messages during or prior to a personal interaction betweenthe first user 106 a and the second user 106 b. The one or more identityconfirmation messages may be directly exchanged via corresponding userinterfaces of the application program 112 presented at the firstelectronic device 104 a and the second electronic device 104 b. The oneor more identity confirmation messages may facilitate the first user 106a and the second user 106 b confirm each other's identity for thepersonal interaction.

In accordance with an embodiment, the second processor 312 may beconfigured to the determine that the first user 106 a and the seconduser 106 b are meeting for a personal interaction based on the trackinginformation captured by the application program 112 of both of the firstelectronic device 104 a and the second electronic device 104 b. Thetracking information may include, for example, date, time, deviceidentifier, and current location of each of the plurality of electronicdevices 104. Accordingly, in an event of a common location, date, andtime of the first electronic device 104 a and the second electronicdevice 104 b from the tracking information captured by the applicationprogram 112, the second processor 312 may determine that the first user106 a and the second user 106 b are meeting for the personalinteraction.

Consequently, the second processor 312 in conjunction with the secondnetwork interface 302 may enable the first electronic device 104 a andthe second electronic device 104 b to directly exchange one or moreidentity confirmation messages to confirm each other's identity for thepersonal interaction, via the application program 112.

In one example, the application program 112, based on an instructionreceived from the second processor 312, may activate a biometric sensor,such as a facial scanner, an iris scanner, or a fingerprint scanner, inthe sensing unit 216 of one or both of the first electronic device 104 aand the second electronic device 104 b. For example, the optical sensorof the first electronic device 104 a may scan, for example, the face,iris, or the fingerprint, of the second user 106 b and the firstprocessor 214 of the first electronic device 104 a may transmit thesensor data to the second processor 312 of the CMS 110. The secondprocessor 312 may compare the sensor data with the user profile of thesecond user 106 b stored in the user profile database 332. The secondprocessor 312 may further confirm that the first user 106 a and thesecond user 106 b are consenting based on data retrieved from theconsent database 336 corresponding to the device identifiers of thefirst electronic device 104 a and the second electronic device 104 b.Accordingly, the second processor 312 in conjunction with the alertengine 330, may transmit an identity verification notification to thefirst electronic device 104 a and the second electronic device 104 b,via the secure communication channel of the wide area network 118 b.

In another example, the application program 112, based on an instructionreceived from the second processor 312, may activate an optical sensor,such as a camera, in the sensing unit 216 of one or both of the firstelectronic device 104 a and the second electronic device 104 b. Forexample, the optical sensor of the first electronic device 104 a maycapture an image of an identification document, for example, a drivinglicense or a social security card, of the second user 106 b and thefirst processor 214 of the first electronic device 104 a may transmitthe image data to the second processor 312 of the CMS 110. The secondprocessor 312 may compare the image data with the user profile of thesecond user 106 b stored in the user profile database 332. The secondprocessor 312 may further confirm that the first user 106 a and thesecond user 106 b are consenting based on data retrieved from theconsent database 336 corresponding to the device identifiers of thefirst electronic device 104 a and the second electronic device 104 b.Accordingly, the second processor 312 in conjunction with the alertengine 330 may transmit an identity verification notification to thefirst electronic device 104 a and the second electronic device 104 b,via the secure communication channel of the wide area network 118 b.

In another example, the application program 112, based on an instructionreceived from the second processor 312, may activate an optical sensor,such as a QR code scanner, in the sensing unit 216 of the firstelectronic device 104 a. The QR code may be already sent by the secondprocessor 312 to both of the first electronic device 104 a and thesecond electronic device 104 b when the consent response is evaluated bythe second processor 312 to be accepted. For example, the QR codescanner of the first electronic device 104 a may scan the QR codedisplayed on the display screen of the second electronic device 104 b.The QR scan image data may be transmitted to the second processor 312 ofthe CMS 110. The second processor 312 may confirm the matching of the QRscan image data and further confirm that the first user 106 a and thesecond user 106 b are consenting by retrieving data from the consentdatabase 336 based on the device identifiers of the first electronicdevice 104 a and the second electronic device 104 b. Accordingly, thesecond processor 312 in conjunction with the alert engine 330, maytransmit an identity verification notification to the first electronicdevice 104 a and the second electronic device 104 b, via the securecommunication channel of the wide area network 118 b.

In yet another example, the first user 106 a and the second user 106 bmay tap their respective electronic devices with each other or placetheir respective electronic devices in proximity. Accordingly, therespective accelerometers or the proximity sensors may trigger theapplication program 112 to transmit the required information, i.e.,date, time, location, and the like, to the CMS 110. The second processor312 may confirm the identities of the consenting first user 106 a andthe second user 106 b based on data retrieved from the user profiledatabase 332 and the consent database 336 corresponding to the deviceidentifiers of the first electronic device 104 a and the secondelectronic device 104 b. Accordingly, the second processor 312 inconjunction with the alert engine 330, may transmit an identityverification notification to the first electronic device 104 a and thesecond electronic device 104 b, via the secure communication channel ofthe wide area network 118 b.

At 428, during the personal interaction between the first user 106 a andthe second user 106 b, the uttered speech signals and the sensor data ofat least the first user 106 a may be received from at least the firstelectronic device 104 a, via the secure communication channel. Inaccordance with an embodiment, the second processor 312 in conjunctionwith the second network interface 302 of the CMS 110, may be configuredto receive, during the personal interaction between the first user 106 aand the second user 106 b, the uttered speech signals and the sensordata of at least the first user 106 a from at least the first electronicdevice 104 a, via the secure communication channel.

In accordance with an embodiment, as the personal interaction betweenthe first user 106 a and the second user 106 b initiates, theapplication program 112 installed at one or both of the first electronicdevice 104 a and the second electronic device 104 b may activate variousdevices corresponding to the sensing unit 216 and/or the input unit 218to capture different types of data associated with the first user 106 aand the second user 106 b. For example, an optical sensor, such as acamera, may get activated to capture the facial expressions of the firstuser 106 a and the second user 106 b. Further, a transducer, such as amicrophone, may also get activated to capture the uttered speech signalsof both the first user 106 a and the second user 106 b during thepersonal interaction. Further, various biometric sensors in the sensingunit 216 may get triggered to measure various bio-signals,psychophysical characteristics or parameters, and behavioral biometricdata of both the first user 106 a and the second user 106 b. All suchuttered speech signals and sensor data may be transmitted by the firstprocessor 214 of at least the first electronic device 104 a to thesecond processor 312 of the CMS 110, via the secure communicationchannel.

At 430, current set of user characteristics may be identified inreal-time based on time sequence of user interactions and uttered speechsignals of each user during the personal interaction. In accordance withan embodiment, the second processor 312 in conjunction with the AIengine 316, the ASR system 318, the NLP engine 320, and the userrecognizer 322 of the CMS 110, may be configured to identify the currentset of user characteristics based on time sequence of user interactionsand uttered speech signals of each user during the personal interaction.

More specifically, the AI engine 316, the ASR system 318 and the NLPengine 320 may analyze the uttered speech signals and associatedemotions during the personal interaction and identify the usercharacteristics in real-time based on time sequence of userinteractions. The user recognizer 322 may analyze the tone, amplitude,and pitch of voice in the uttered speech signals. Accordingly, thesecond processor 312 in conjunction with the AI engine 316, maycorrelate each of the uttered speech signals with corresponding user ina time sequence-based manner and store in the user profile database 332.

In accordance with an embodiment, the user characteristics of each usermay be identified in real-time based on time sequence of userinteractions and uttered speech signals of each user. Examples of suchuser characteristics of each user may include current state of, forexample, geographical origin, behaviour, state of mind, mental health,physical health, level of intoxication, moods and emotions, sleepinessand fatigue, and personality traits (for example, sociability, activity,aggression-hostility, impulsive sensation seeking, andneuroticism-anxiety). For example, current psychological state of thefirst user 106 a may be hesitant due to speech errors and irregularities(such as, number of false and unintelligible words, and interrupts),current level of intoxication is high due to abrupt rhythmic features ofspeech, current level of physical state may be drugged due to voicehoarseness and additional sounds, like coughs and sniffles, currentstate of emotion may be disgust based on voice pitch variations, type ofwords used on the speech and speech energy levels, and the like, may beidentified. Various features, for example, speaking rate, loudness,spectral features, and characteristics of linguistic expression, may beapplied for the inference of the personality traits.

At 432, new set of user characteristics for the first user 106 a and thesecond user 106 b may be predicted during the personal interaction basedon time sequence-based personal interaction data, training data set, andartificial neural network. In accordance with an embodiment, the secondprocessor 312 in conjunction with the ML engine 314, the AI engine 316,the ASR system 318, the NLP engine 320, and the user recognizer 322 ofthe CMS 110, may be configured to predict the user characteristics forthe first user 106 a and the second user 106 b during the personalinteraction based on time sequence-based personal interaction data,training data set, and artificial neural network. The current set ofuser characteristics may be utilized as a training data set for theprediction of the new set of user characteristics. In accordance with anembodiment, the prediction of the new set of user characteristics may befurther based on social media data of the first user 106 a and thesecond user 106 b collected from one or more public informationdatabases. The social media data may include a plurality of mediashared, content posts, social media contacts having a predefined socialmedia distance between user accounts and information relating to thesocial media contacts.

In accordance with an embodiment, the second processor 312 inconjunction with the ML engine 314, the AI engine 316, the ASR system318, the NLP engine 320, and the user recognizer 322, may build aprediction model and a model evaluation system, and combine with anartificial neural network algorithm to predict new set of usercharacteristics for the first user 106 a and the second user 106 b. Fortraining the prediction model, current set of user characteristics ofthe first user 106 a and the second user 106 b identified during thepersonal interaction may be used for accurate results. The prediction ofthe new set of user characteristics for the first user 106 a and thesecond user 106 b may provide a likelihood that the first user 106 a andthe second user 106 b will successively perform one or more activitiesduring the personal interaction.

At 434 of the flowchart 400C in FIG. 4C, a confidence score may bedetermined based on intent of each of the first user 106 a and thesecond user 106 b, current sensor data, and the new set of usercharacteristics for the first user 106 a and the second user 106 bduring the personal interaction. In accordance with an embodiment, thesecond processor 312 in conjunction with the ML engine 314, the AIengine 316, the ASR system 318, the NLP engine 320, and the userrecognizer 322 of the CMS 110, may be configured to determine theconfidence score based on the intent of each of the first user 106 a andthe second user 106 b, the current sensor data, and the new set of usercharacteristics for the first user 106 a and the second user 106 bduring the personal interaction.

The intent of the first user 106 a and the second user 106 b may bederived by the NLP engine 320 based on a domain, one or more tasks, andone or more parameters required to accomplish the one or more tasks. Thedomain may correspond to performing one or more activities during thepersonal interaction based on the acceptance of the consent request.Examples of the one or more tasks may include reserving a table or aroom, purchasing a present for gifting, playing romantic songs, and thelike. Examples of the one or more parameters required to accomplish theone or more tasks my include (date, time, number of persons) forreserving a table or a room, (date, time, type of present) for gifting.The intent of the first user 106 a and the second user 106 b may befurther determined based on the positive conversation between the firstuser 106 a and the second user 106 b during the personal interaction.For example, the first user 106 a and the second user 106 b are willingfor a next activity when one activity is performed.

The current sensor data may correspond to the immediate values measuredby the sensing unit 216 of both of the first electronic device 104 a andthe second electronic device 104 b. The current sensor data may include,for example, psychophysical characteristics or parameters,movement-related metrics of both the first user 106 a and the seconduser 106 b and behavioral identifiers unique to both the first user 106a and the second user 106 b.

Based on the intent of each of the first user 106 a and the second user106 b, the current sensor data, and the new set of user characteristicsfor the first user 106 a and the second user 106 b during the personalinteraction, the confidence score may be determined. The confidencescore may represent a likelihood that the personal interaction isproceeding in accordance with or against the acceptance of the initialconsent request.

In accordance with an embodiment, the second processor 312 inconjunction with the ML engine 314, may determine the confidence scorebased on a statistical classification technique. Examples of thestatistical classification technique may include, but are not limitedto, a maximum-entropy classifier, a logistic regression classifier, aNaive Bayes classifier, and a support vector machine.

At 436, an immediate consent or dissent of the first user 106 a or thesecond user 106 b may be detected at defined timestamp during personalinteraction based on the comparison of confidence score with thresholdvalue, one or more explicit or implied keywords from uttered speechsignals, and an extent of deviated values of the sensor data. Inaccordance with an embodiment, the consent detection engine 324 of theCMS 110 may be configured to detect an immediate consent or dissent ofthe first user 106 a or the second user 106 b at the defined timestampduring the personal interaction based on the comparison of theconfidence score with the threshold value, one or more explicit orimplied keywords from uttered speech signals, and an extent of deviatedvalues of the sensor data.

In accordance with an embodiment, the consent detection engine 324 maybe configured to detect the immediate consent of the first user 106 a orthe second user 106 b at the defined timestamp during the personalinteraction based on the value of the confidence score exceeding thethreshold value, one or more explicit or implied positive keywords fromuttered speech signals, and an extent of deviated values of the sensordata being less than a threshold sensor value. In an example, theconfidence score representing the likelihood that the personalinteraction between the first user 106 a and the second user 106 b isproceeding in accordance with the acceptance of the initial consentrequest exceeds the threshold value. Further, one or more explicit orimplied positive keywords, for example, “yes,” “I'm sure,” “I do,” “Iwant to,” “Don't stop.” “Definitely,” and the like, may be detected bythe ASR system 318 and the NLP engine 320 from the uttered speechsignals. Furthermore, the extent of deviated values of the sensor datais determined to be less than the threshold sensor value, which impliesthat there may be a minor anomaly but overall, the deviated values ofthe sensor data are acceptable. Based on the above, the consentdetection engine 324 may be configured to detect an immediate consent ofthe first user 106 a or the second user 106 b at the defined timestampduring the personal interaction.

In accordance with another embodiment, the consent detection engine 324may be configured to detect an immediate dissent of the first user 106 aor the second user 106 b at the defined timestamp during the personalinteraction based on the value of the confidence score being less thanthe threshold value, one or more explicit or implied negative keywordsfrom uttered speech signals, and the extent of deviated values of thesensor data exceeding the threshold sensor value. In an example, theconfidence score representing the likelihood that the personalinteraction between the first user 106 a and the second user 106 b isproceeding against the acceptance of the initial consent request exceedsthe threshold value. Further, one or more explicit or implied negativekeywords, for example, “no”, “stop”, “I don't want to”, “this is makingme uncomfortable”, “wait”, “it's wrong”, “back off”, “leave me”, “goaway”, or any type of abusive/threatening/distressing words and thelike, may be detected by the ASR system 318 and the NLP engine 320 fromthe uttered speech signals. Furthermore, the extent of deviated valuesof the sensor data is determined to be exceeding the threshold sensorvalue, which implies that there is a major anomaly and overall, thedeviated values of the sensor data are not acceptable. Based on theabove, the consent detection engine 324 may be configured to detect animmediate dissent of the first user 106 a or the second user 106 b atthe defined timestamp during the personal interaction.

At 438, the immediate consent or dissent of the first user 106 a or thesecond user 106 b may be validated based on a plurality of criteria. Inaccordance with an embodiment, the consent validation engine 326 of theCMS 110 may be configured to validate the immediate consent or dissentof the first user 106 a or the second user 106 b based on the pluralityof criteria. In accordance with an embodiment, the plurality of criteriafor the validation of the detected immediate consent or dissent of thesecond user 106 b may comprise at least informed, freely given,reversible, enthusiastic, and specific agreement between the first user106 a and the second user 106 b to perform one or more activities duringthe personal interaction. A valid consent is freely given, i.e., toengage in a physical relation (such as sexual activities) is a decisionthat should be made without pressure, force, manipulation, or while theuser is mentally or physically capacitated (for example, not in sleep,unconscious, and intoxicated due to drugs, alcohol, or other reasons).The valid consent is reversible, i.e., anyone can change their mindabout what they want to do, at any time, even in the middle ofperforming an activity. The valid consent is informed, i.e., enoughinformation is to be provided to enable the user to gain a genuineunderstanding of the nature and effects of the activities to beperformed during the personal interaction. The valid consent isenthusiastic, i.e., only “yes” means “yes,” therefore consent cannot beinferred from silence, passivity or lack of resistance, or lack ofactive response. The valid consent is specific, i.e., to be firm insetting boundaries and making it clear about what a user will or willnot engage in. It must be clearly demonstrated through words and/oractions as mutually understood by both users.

In accordance with an embodiment, the validation of the immediateconsent or dissent may be evaluated based on a weighted scoring methodapplied on various features of the plurality of criteria. The method mayinclude computing a plurality of weights using training data and machinelearning techniques by the ML engine 314. The plurality of criteria maybe weighted where the weight signifies the absolute/relativesignificance of the criterion. In accordance with an embodiment, theweights may be adjusted automatically, using known methods of supervisedtraining. Accordingly, a weighted combination (for example a weightedaverage) may be computed that may indicate a validation score.

In accordance with an embodiment, the validation score may be less thana threshold value. In such an embodiment, the consent validation engine326 may indicate that the one of the first user 106 a and the seconduser 106 b has provided immediate dissent for one or more activitiesduring the personal interaction. Accordingly, the control passes to step440.

In accordance with another embodiment, the validation score may exceedthe threshold value. In such an embodiment, the consent validationengine 326 may indicate that the one of the first user 106 a and thesecond user 106 b has provided immediate consent for one or moreactivities during the personal interaction. Accordingly, the controlpasses to step 444.

At 440, the acceptance on the consent request may be overridden by theimmediate dissent of one of the first user 106 a or the second user 106b for performing an activity during the personal interaction. Inaccordance with an embodiment, the second processor 312 of the CMS 110may be configured to override the acceptance on the consent request bythe immediate dissent of one of the first user 106 a or the second user106 b for performing an activity during the personal interaction. Thesecond processor 312 of the CMS 110 may be further configured to storethe overridden acceptance on the consent request by the immediatedissent of the one of the first user 106 a or the second user 106 b inthe consent database 336.

At 442, a second set of tasks may be performed based on the validationof the detected dissent of one of the first user 106 a or the seconduser 106 b. In accordance with an embodiment, the second processor 312in conjunction with the task manager 328 of the CMS 110, may beconfigured to perform the second set of tasks based on the validation ofthe detected dissent of one of the first user 106 a or the second user106 b for performing an activity during the personal interaction. Thesecond set of tasks may be performed by the task manager 328 based on auser request by the first user 106 a or the second user 106 b. Forexample, the second set of tasks may be publishing of time sequencerecording of conversation incurred during the personal interaction.Further, other tasks from the second set of tasks performed based on thevalidation of the immediate dissent of one of the first user 106 a orthe second user 106 b may comprise dialing an emergency number ofcurrent location, dialing a number of an emergency contact person, oractivating an emergency alarm sound.

At 444, based on the user request, time sequence recording of theconversation incurred during the personal interaction may be published.In accordance with an embodiment, the second processor 312 inconjunction with the task manager 328 of the CMS 110, may be configuredto publish, based on the user request, time sequence recording of theconversation incurred during the personal interaction.

In an exemplary scenario, the user request may be generated by thesecond user 106 b for publishing the time sequence recording of theconversation incurred during the personal interaction between the firstuser 106 a and the second user 106 b. Such published time sequencerecording of the conversation may act as a legally sound evidence infavour of the second user 106 b to level a genuine accusation on thefirst user 106 a in case the first user 106 a has committed, forexample, a misconduct, delinquency, harassment, assault, or othercriminal activity, despite of the dissent of the second user 106 bduring the personal interaction.

In an exemplary scenario, the user request may be generated by the firstuser 106 a in case the second user 106 b comes out with a falseaccusation against the first user 106 a about, for example, amisconduct, delinquency, harassment, assault, or other criminalactivity, during the personal interaction. In such case, the publishedtime sequence recording of the conversation incurred during the personalinteraction between the first user 106 a and the second user 106 b mayfunction as legally sound evidence in favour of the first user 106 a andagainst the second user 106 b.

FIG. 5A illustrates a first sequence diagram 500A for operational stepsperformed between the first electronic device 104 a, the secondelectronic device 104 b, and the CMS 110 for consent request generatedby the first electronic device 104 a, and consent response (acceptanceor rejection) generated by the second electronic device 104 b, inaccordance with an exemplary embodiment of the disclosure.

Once the secure communication channel is established (S1), the firstuser 106 a may select the second user profile of the second user 106 bin accordance with various embodiments, as described in FIG. 4A. Basedon a selection of the user profile of the second user 106 b, the firstuser 106 a may generate a consent request, addressed for the second user106 b, at the first electronic device 104 a. The consent request may betransmitted by the first electronic device 104 a to the CMS 110 (S2).The consent request may be generated by the first user 106 a at thefirst electronic device 104 a based on a manipulation of a selectableoption, such as a software button presented on the user interface of theapplication program 112 at the first electronic device 104 a.

The CMS 110 may further transmit the consent request to the secondelectronic device 104 b (S3). The CMS 110 may store date and time of thereceipt and transmittal of the consent request along with other details,such as the unique sender and receiver device identifiers and thelocation of the first electronic device 104 a and the second electronicdevice 104 b, in the consent database 336 for future references (S4).

The second electronic device 104 b may receive the consent request fromthe CMS 110 and generate a consent response corresponding to the consentrequest based on options selected or data provided by the second user106 b. In accordance with an embodiment, the consent response maycorrespond to acceptance of the consent request when the second user 106b manipulates selectable options, such as a yes/accept software buttons,in the consent request presented at the user interface of theapplication program 112 at the second electronic device 104 b. Inaccordance with another embodiment, the consent response may correspondto rejection of the consent request when the second user 106 bmanipulates selectable options, such as no/reject software buttons, inthe consent request presented at the user interface of the applicationprogram 112 at the second electronic device 104 b.

The second electronic device 104 b may transmit the consent response(acceptance or rejection) to the CMS 110 (S5). The CMS 110 may generatenotification corresponding to the consent response (acceptance orrejection) and transmit the generated notifications to both of the firstelectronic device 104 a and the second electronic device 104 b (S6) and(S7).

The CMS 110 may store date and time of the consent response (acceptanceor rejection) along with other details, such as the unique sender andreceiver device identifiers and the location of the first electronicdevice 104 a and the second electronic device 104 b, in the consentdatabase 336 for future references (S8).

FIG. 5B illustrates a second sequence diagram 500B for operational stepsperformed between the first electronic device 104 a, the secondelectronic device 104 b, and the CMS 110 for consent request generatedand cancelled by the first electronic device 104 a, in accordance withan exemplary embodiment of the disclosure.

The operational steps (S1) to (S4) performed in the second sequencediagram 500B are similar to the operational steps (S1) to (S4) performedin the first sequence diagram 500A. In accordance with an embodiment,the first user 106 a may cancel or withdraw the consent request(addressed for the second user 106 b) based on a manipulation of aselectable option presented on the user interface of the applicationprogram 112 at the first electronic device 104 a. The consentcancellation or withdrawal request may be transmitted by the firstelectronic device 104 a to the CMS 110 (S9).

On priority basis, the CMS 110 may override the initial consent requestwith the consent cancellation or withdrawal request, and furthertransmit the notifications regarding the consent cancellation orwithdrawal to the first electronic device 104 a and second electronicdevice 104 b (S10) and (S11). The CMS 110 may store date and time of thereceipt and transmittal of the consent cancellation or withdrawalrequest along with other details, such as the unique sender and receiverdevice identifiers and the location of the first electronic device 104 aand the second electronic device 104 b, in the consent database 336 forfuture references (S12).

FIG. 5C illustrates a third sequence diagram 500C for operational stepsperformed between the first electronic device 104 a, the secondelectronic device 104 b, and the CMS 110 for consent negotiation, inaccordance with an exemplary embodiment of the disclosure.

Once the secure communication channel is established (S20), the CMS 110may receive detailed information pertaining to the plurality of users106 (such as the first user 106 a and the second user 106 b) from theplurality of electronic devices 104 (such as the first electronic device104 a and the second electronic device 104 b) (S21) and (S22), via thesecure communication channel. The CMS 110 may be configured to generatea plurality of user profiles based on the detailed informationpertaining to the plurality of users 106 received from the plurality ofelectronic devices 104 (S23). The CMS 110 may further store theplurality of user profiles in the user profile database 332 (S24). Inaccordance with an embodiment, the first user 106 a may select thesecond user profile of the second user 106 b in accordance with variousembodiments, as described in FIG. 4A (S25). The CMS 110 may furtherenable the first electronic device 104 a and the second electronicdevice 104 b to exchange plurality of messages prior to personalinteraction between the first user 106 a and the second user 106 b(S26). For the second user profile of the second user 106 b, the firstuser 106 a may generate a consent request, addressed for the second user106 b, at the first electronic device 104 a. The consent request may begenerated by the first user 106 a at the first electronic device 104 abased on a manipulation of a selectable option, such as a softwarebutton, presented on the user interface of the application program 112at the first electronic device 104 a. The consent request may betransmitted by the first electronic device 104 a to the CMS 110 (S27).

The CMS 110 may further transmit the consent request to the secondelectronic device 104 b (S28). The CMS 110 may store date and time ofthe receipt and transmittal of the consent request along with otherdetails, such as the unique sender and receiver device identifiers andthe location of the first electronic device 104 a and the secondelectronic device 104 b, in the consent database 336 for futurereferences (S29).

In accordance with an embodiment, the second electronic device 104 b mayreceive the consent request from the CMS 110 and generate a consentresponse corresponding to the received consent request based on optionsselected or data provided by the second user 106 b (S30). The consentresponse may initiate a loop (L1) for a negotiation between the firstuser 106 a and the second user 106 b, via the CMS 110. The negotiationmay be performed as the second user 106 b revises the consent request byediting the terms, conditions, and/or clauses of the mutual agreement inthe received consent request. The second electronic device 104 b maytransmit the consent response as a revised consent request to the CMS110 (S31).

The CMS 110 may further transmit the revised consent request to thefirst electronic device 104 a (S32). The CMS 110 may also store date andtime of the receipt and transmittal of the revised consent request alongwith other details, such as the unique sender and receiver deviceidentifiers and the location of the first electronic device 104 a andthe second electronic device 104 b, in the consent database 336 forfuture references (S33).

In accordance with an embodiment, the first user 106 a at the firstelectronic device 104 a may accept or reject the revised consent requestbased on options selected or data provided by the first user 106 a(S34). The first electronic device 104 a may transmit the revisedconsent response that may correspond to acceptance or rejection of therevised consent request. The revised consent response (acceptance orrejection) may be transmitted to the CMS 110 (S35). Accordingly, theloop (L1) is terminated, and control exits the loop (L1) (S36). The CMS110 may generate notification corresponding to the revised consentresponse (acceptance or rejection) and transmit the generatednotifications to both of the first electronic device 104 a and thesecond electronic device 104 b (S37) and (S38). In accordance with anembodiment, the CMS 110 stores date and time of the receipt of therevised consent response (acceptance or rejection) along with otherdetails, such as the unique sender and receiver device identifiers andthe location of the first electronic device 104 a and the secondelectronic device 104 b, in the consent database 336 for futurereferences (S39).

In accordance with another embodiment, the first user 106 a at the firstelectronic device 104 a may review the revised consent request and editthe revised consent request based on options selected or data providedby the first user 106 a (S34). The counter revised consent request maybe transmitted to the CMS 110 (S35). The CMS 110 may further transmitthe counter revised consent request to the second electronic device 104b (S40). The loop (L1) is executed again as the consent request isiteratively revised by one electronic device and responded to by otherelectronic device until accepted by both of the first user 106 a and thesecond user 106 b on a set of terms, conditions, and clauses acceptableto both. For example, after a defined number of iterations, the secondelectronic device 104 b may transmit a final consent response(acceptance or rejection) to the CMS 110 (S41). As described above, theCMS 110 may generate notification corresponding to the consent response(acceptance or rejection) and transmit the generated notifications toboth of the first electronic device 104 a and the second electronicdevice 104 b (S37) and (S38). In accordance with another embodiment, theCMS 110 may store date and time of the receipt and transmittal of thefinal consent response and the complete chain of negotiations along withother details, such as the unique sender and receiver device identifiersand the location of the first electronic device 104 a and the secondelectronic device 104 b, in the consent database 336 for futurereferences (S39).

FIG. 5D illustrates a fourth sequence diagram 500D for operational stepsperformed between the first electronic device 104 a, the secondelectronic device 104 b, and the CMS 110 for consent detection andvalidation, in accordance with an exemplary embodiment of thedisclosure.

Once both of the first electronic device 104 a and the second electronicdevice 104 b receive notification from the CMS 110 (S42) and (S43), thefirst user 106 a and the second user 106 b may start a new relationshipby exchanging a plurality of messages, for example, text messages,personal emails, phone calls, video calls, and the like, via the securecommunication channel. The exchange of the plurality of messages may befacilitated by the CMS 110 (S44) and (S45). The exchange of theplurality of messages may be further stored by the CMS 110 in theadditional database 338 (S46).

In accordance with an embodiment, the CMS 110 may determine an eventbased on an analysis of the plurality of messages using natural languageprocessing techniques (S47). Corresponding to the event, the CMS 110 maygenerate a first set of tasks, for example, generating calendar entry,booking a cab, a table, and/or a lodging place, for the personalinteraction between the first user 106 a and the second user 106 b andsend notification about the generated first set of tasks to both of thefirst electronic device 104 a and the second electronic device 104 b(S48) and (S49).

The CMS 110 may further enable the first user 106 a and the second user106 b for a direct exchange of one or more identity confirmationmessages presented on corresponding user interfaces of the applicationprogram 112 presented on the first electronic device 104 a and thesecond electronic device 104 b respectively, during or prior to thepersonal interaction between the first user 106 a and the second user106 b (S50) and (S51).

During the personal interaction between the first user 106 a and thesecond user 106 b, the CMS 110 may receive the uttered speech signalsand the sensor data of one or both of the first user 106 a and thesecond user 106 b from one or both of the first electronic device 104 aand the second electronic device 104 b, via the secure communicationchannel with one or both of the first electronic device 104 a and thesecond electronic device 104 b (S52) and (S53).

The CMS 110 may identify current set of the user characteristics basedon time sequence of user interactions and uttered speech signals of eachuser during the personal interaction (S54). The CMS 110 may furtherpredict the new set of user characteristics for the first user 106 a andthe second user 106 b during the personal interaction based on timesequence-based personal interaction data, the training data set, andartificial neural network (555).

The CMS 110 may further determine the confidence score based on theintent of each of the first user 106 a and the second user 106 b, thecurrent sensor data, and the new set of user characteristics for thefirst user 106 a and the second user 106 b during the personalinteraction (556).

Based on the comparison of the confidence score with the thresholdvalue, one or more explicit or implied keywords from uttered speechsignals, and an extent of deviated values of the sensor data, the CMS110 may detect an immediate consent or dissent of the first user 106 aor the second user 106 b at the defined timestamp during the personalinteraction (557). The CMS 110 may further validate the immediateconsent or dissent of the first user 106 a or the second user 106 bbased on the plurality of criteria (558).

In accordance with an embodiment, the CMS 110 may override theacceptance on the consent request by the immediate dissent of the firstuser 106 a or the second user 106 b for performing an activity duringthe personal interaction (559).

Based on the validation of the detected dissent of the first user 106 aor the second user 106 b for performing an activity during the personalinteraction, the CMS 110 may perform a second set of tasks (S60). TheCMS 110 may publish, based on the user request of the first user 106 aor the second user 106 b, time sequence recording of the conversationincurred during the personal interaction (S61).

FIG. 6 is a conceptual diagram illustrating an example of a hardwareimplementation for the exemplary electronic device of FIG. 2A employinga processing system, in accordance with an exemplary embodiment of thedisclosure. Referring to FIG. 6 , the hardware implementation shown by arepresentation 600 for the exemplary electronic device 202, such as thefirst electronic device 104 a and the second electronic device 104 b,employs a first processing system 602 for consent detection andvalidation, in accordance with an exemplary embodiment of thedisclosure, as described herein.

In some examples, the first processing system 602 may comprise one ormore hardware processors, such as a first hardware processor 604, anon-transitory first computer readable medium 606, a first bus 608, afirst bus interface 610, a first transceiver 612, and the first memory206. FIG. 6 further illustrates the first network interface 204, thefirst processor 214, the first memory 206, the sensing unit 216, theinput unit 218, and the output unit 220, as described in detail in FIG.2A.

The first hardware processor 604 may be configured to execute orimplement software, hardware, and/or firmware modules and manage thefirst bus 608 and general processing, including the execution of a setof instructions stored on the non-transitory first computer readablemedium 606. The set of instructions, when executed by the first hardwareprocessor 604, causes the CMS 110 to execute the various operationsdescribed herein for any particular apparatus. The first hardwareprocessor 604 may be implemented, based on a number of processortechnologies known in the art. Examples of the hardware processorrealized as the first hardware processor 604 may be a RISC processor, anASIC processor, a CISC processor, and/or other processors or controlcircuits. In accordance with various embodiment, the first hardwareprocessor 604 may include a single or multiple set of processors ormulti-core processors. Moreover, the first hardware processor 604 may beimplemented as an integrated processing system and/or a distributedprocessing system.

The non-transitory first computer readable medium 606 may be used forstoring data that is manipulated by the first hardware processor 604when executing the set of instructions. The data is stored for shortperiods or in the presence of power. The non-transitory first computerreadable medium 606 may also be configured to store data for the firstnetwork interface 204, the first processor 214, the first memory 206,the sensing unit 216, the input unit 218, and the output unit 220, asdescribed in detail in FIG. 2A.

As described above, the first memory 206 may store local versions ofapplications being executed by the first hardware processor 604, relatedinstructions and corresponding parameters. The first memory 206 mayinclude a type of memory usable by a computer, such as RAM, ROM, tapes,magnetic discs, optical discs, volatile memory, non-volatile memory, andany combination thereof. Additionally, the first hardware processor 604and the first memory 206 may include and execute an operating systemexecuting on the first hardware processor 604, one or more applicationsand display drivers and/or other components.

The first bus 608 is configured to link together various circuits. Inthis example, the CMS 110 employing the first processing system 602, thefirst hardware processor 604, the non-transitory first computer readablemedium 606, and the first memory 206 may be implemented with busarchitecture, represented by first bus 608. The first bus 608 mayinclude any number of interconnecting buses and bridges depending on thespecific implementation of the CMS 110 and the overall designconstraints. The first bus interface 610 may be configured to provide aninterface between the first bus 608 and other circuits, such as, thefirst transceiver 612.

The first transceiver 612 may be configured to provide a communicationof the CMS 110 with various other external systems. The firsttransceiver 612 may communicate via wireless communication withnetworks, such as the Internet, the Intranet and/or a wireless network,such as a cellular telephone network, WLAN and/or a MAN. The wirelesscommunication may use any of a plurality of communication standards,protocols and technologies, such as GSM, Enhanced Data GSM Environment(EDGE), LTE, wideband code division multiple access (W-CDMA), CDMA, timedivision multiple access (TDMA), Bluetooth©, Wi-Fi (such as IEEE802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice overInternet Protocol (VoIP), and/or Wi-MAX. In accordance with anembodiment, the first transceiver 612 may provide for establishing andmaintaining communications, such as UWB communications, with one or moreother devices, parties, entities, and the like, utilizing hardware,software, and services. For example, the first transceiver 612 mayprovide for establishing and maintaining the short- or long-rangecommunication protocols.

It should be recognized that, in some embodiments of the disclosure, oneor more components of FIG. 6 may include software whose correspondingcode may be executed by at least one processor, across multipleprocessing environments. For example, the first network interface 204,the first processor 214, the first memory 206, the sensing unit 216, theinput unit 218, and the output unit 220 may include software that may beexecuted across a single or multiple processing environments.

In an aspect of the disclosure, the first hardware processor 604, thenon-transitory first computer readable medium 606, or a combination ofboth may be configured or otherwise specially programmed to execute theoperations or functionality of the first network interface 204, thefirst processor 214, the first memory 206, the sensing unit 216, theinput unit 218, and the output unit 220, or various other componentsdescribed herein, as described with respect to FIG. 2A.

FIG. 7 is a conceptual diagram illustrating an example of a hardwareimplementation for an exemplary consent management system, such as theCMS 110 of FIGS. 3A and 3B, employing a processing system for consentdetection and validation, in accordance with an exemplary embodiment ofthe disclosure. Referring to FIG. 7 , the hardware implementation shownby a representation 700 for the CMS 110 employs a second processingsystem 702 for consent detection and validation, in accordance with anexemplary embodiment of the disclosure, as described herein.

In some examples, the second processing system 702 may comprise one ormore hardware processors, such as a second hardware processor 704, anon-transitory second computer readable medium 706, the second memory304, a second bus 708, a second bus interface 710, and a secondtransceiver 712. FIG. 7 further illustrates the second network interface302 and the second memory 304 (comprising the second RAM 306, the secondROM 308, and the second program data 310), the second processor 312, theML engine 314, the AI engine 316, the ASR system 318 (comprising theacoustic frontend 340, the speech recognition engine 342, the acousticmodel 344, the token manager 346, the pronunciation dictionary 348, andthe language model 350), the NLP engine 320 (comprising the morphemeanalyzer 352, the grammar module 354, the syntax parser 356, and theintent analyzer 358), the user recognizer 322, the consent detectionengine 324, the consent validation engine 326, the task manager 328, thealert engine 330, the user profile database 332, the task list database334, the consent database 336, and the additional database 338, asdescribed in detail in FIG. 3A.

The second hardware processor 704 may be configured to execute orimplement software, hardware, and/or firmware modules and manage thesecond bus 708 and general processing, including the execution of a setof instructions stored on the non-transitory second computer readablemedium 706. The set of instructions, when executed by the secondhardware processor 704, causes the CMS 110 to execute the variousoperations described herein for any particular apparatus. The secondhardware processor 704 may be implemented, based on a number ofprocessor technologies known in the art. Examples of the hardwareprocessor realized as the second hardware processor 704 may be a RISCprocessor, an ASIC processor, a CISC processor, and/or other processorsor control circuits. In accordance with various embodiment, the secondhardware processor 704 may include a single or multiple set ofprocessors or multi-core processors. Moreover, the second hardwareprocessor 704 may be implemented as an integrated processing systemand/or a distributed processing system.

The non-transitory second computer readable medium 706 may be used forstoring data that is manipulated by the second hardware processor 704when executing the set of instructions. The data is stored for shortperiods or in the presence of power. The non-transitory second computerreadable medium 706 may also be configured to store data for the secondnetwork interface 302 and the second memory 304 (comprising the secondRAM 306, the second ROM 308, and the second program data 310), thesecond processor 312, the ML engine 314, the AI engine 316, the ASRsystem 318 (comprising the acoustic frontend 340, the speech recognitionengine 342, the acoustic model 344, the token manager 346, thepronunciation dictionary 348, and the language model 350), the NLPengine 320 (comprising the morpheme analyzer 352, the grammar module354, the syntax parser 356, and the intent analyzer 358), the userrecognizer 322, the consent detection engine 324, the consent validationengine 326, the task manager 328, the alert engine 330, the user profiledatabase 332, the task list database 334, the consent database 336, andthe additional database 338, as described in detail in FIG. 3A.

As described above, the second memory 304 may store local versions ofapplications being executed by the first hardware processor 604, relatedinstructions and corresponding parameters. The first memory 206 mayinclude a type of memory usable by a computer, such as RAM, ROM, tapes,magnetic discs, optical discs, volatile memory, non-volatile memory, andany combination thereof. Additionally, the first hardware processor 604and the first memory 206 may include and execute an operating systemexecuting on the first hardware processor 604, one or more applicationsand display drivers and/or other components.

The second bus 708 is configured to link together various circuits. Inthis example, the CMS 110 employing the second processing system 702,the second hardware processor 704, the non-transitory second computerreadable medium 706, and the second memory 304 may be implemented withbus architecture, represented by second bus 708. The second bus 708 mayinclude any number of interconnecting buses and bridges depending on thespecific implementation of the CMS 110 and the overall designconstraints. The second bus interface 710 may be configured to providean interface between the second bus 708 and other circuits, such as, thesecond transceiver 712.

The second transceiver 712 may be configured to provide a communicationof the CMS 110 with various other external systems. The secondtransceiver 712 may communicate via wireless communication withnetworks, such as the Internet and/or a wireless network, such as acellular telephone network, WLAN and/or a MAN. The wirelesscommunication may use any of a plurality of communication standards,protocols, and technologies, such as GSM, EDGE, LTE, W-CDMA, CDMA, TDMA,Bluetooth©, Wi-Fi (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11gand/or IEEE 802.11n), VoIP, and/or Wi-MAX. In accordance with anembodiment, the second transceiver 712 may provide for establishing andmaintaining communications, such as UWB communications, with one or moreother devices, parties, entities, and the like, utilizing hardware,software, and services. For example, the second transceiver 712 mayprovide for establishing and maintaining the short-range or long-rangecommunication protocols.

It should be recognized that, in some embodiments of the disclosure, oneor more components of FIG. 7 may include software whose correspondingcode may be executed by at least one processor, across multipleprocessing environments. For example, the second network interface 302and the second memory 304 (comprising the second RAM 306, the second ROM308, and the second program data 310), the second processor 312, the MLengine 314, the AI engine 316, the ASR system 318 (comprising theacoustic frontend 340, the speech recognition engine 342, the acousticmodel 344, the token manager 346, the pronunciation dictionary 348, andthe language model 350), the NLP engine 320 (comprising the morphemeanalyzer 352, the grammar module 354, the syntax parser 356, and theintent analyzer 358), the user recognizer 322, the consent detectionengine 324, the consent validation engine 326, the task manager 328, thealert engine 330, the user profile database 332, the task list database334, the consent database 336, and the additional database 338 mayinclude software that may be executed across a single or multipleprocessing environments.

In an aspect of the disclosure, the second hardware processor 704, thenon-transitory second computer readable medium 706, or a combination ofboth may be configured or otherwise specially programmed to execute theoperations or functionality of the second network interface 302 and thesecond memory 304 (comprising the second RAM 306, the second ROM 308,and the second program data 310), the second processor 312, the MLengine 314, the AI engine 316, the ASR system 318 (comprising theacoustic frontend 340, the speech recognition engine 342, the acousticmodel 344, the token manager 346, the pronunciation dictionary 348, andthe language model 350), the NLP engine 320 (comprising the morphemeanalyzer 352, the grammar module 354, the syntax parser 356, and theintent analyzer 358), the user recognizer 322, the consent detectionengine 324, the consent validation engine 326, the task manager 328, thealert engine 330, the user profile database 332, the task list database334, the consent database 336, and the additional database 338, orvarious other components described herein, as described with respect toFIG. 3A.

Various embodiments of the disclosure comprise the CMS 110 that may beconfigured for consent detection and validation. The CMS 110 maycomprise one or more processors, for example, the second networkinterface 302 and the second memory 304 (comprising the second RAM 306,the second ROM 308, and the second program data 310), the secondprocessor 312, the ML engine 314, the AI engine 316, the ASR system 318(comprising the acoustic frontend 340, the speech recognition engine342, the acoustic model 344, the token manager 346, the pronunciationdictionary 348, and the language model 350), the NLP engine 320(comprising the morpheme analyzer 352, the grammar module 354, thesyntax parser 356, and the intent analyzer 358), the user recognizer322, the consent detection engine 324, the consent validation engine326, the task manager 328, the alert engine 330, the user profiledatabase 332, the task list database 334, the consent database 336, andthe additional database 338.

The CMS 110 includes a memory, such as the second memory 304, forstoring instructions and a processor, such as the second processingsystem 702, for executing the instructions. Based on the executedinstructions, one or more processors in the CMS 110 may be configured toreceive, during a personal interaction between the first user 106 a andthe second user 106 b, uttered speech signals and sensor data of atleast the first user 106 a from at least the first electronic device 104a, via a secure communication channel. The personal interaction may bescheduled based on a consent response, received from the secondelectronic device 104 b, corresponding to an acceptance of a consentrequest received from the first electronic device 104 a. The one or moreprocessors in the CMS 110 may be further configured to determine aconfidence score based on an intent of both of the first user 106 a andthe second user 106 b, current sensor data and a new set of usercharacteristics predicted for the first user 106 a and the second user106 b during the personal interaction. The one or more processors in theCMS 110 may be further configured to detect an immediate consent or animmediate dissent of one of the first user 106 a or the second user 106b at a defined timestamp during the personal interaction based on atleast one of a comparison of the confidence score with a thresholdvalue, one or more explicit or implied keywords from the uttered speechsignals, and an extent of deviated values of the sensor data. The one ormore processors in the CMS 110 may be further configured to validate theimmediate consent or the immediate dissent of one of the first user 106a or the second user 106 b based on a plurality of criteria. The one ormore processors in the CMS 110 may be further configured to perform asecond set of tasks based on the validation of the immediate dissent ofone of the first user 106 a or the second user 106 b.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to receive detailed information,pertaining to the plurality of users 106, from the plurality ofelectronic devices 104, via the secure communication channel. Thedetailed information may be captured at the plurality of electronicdevices 104 through the user interface of the application program 112presented at each of the plurality of electronic devices 104 duringregistration. The one or more processors in the CMS 110 may be furtherconfigured to generate a plurality of user profiles based on thedetailed information, pertaining to the plurality of users 106, receivedfrom the plurality of electronic devices 104. The one or more processorsin the CMS 110 may be further configured to store the plurality of userprofiles in a user profile database.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to receive, via the secure communicationchannel, the consent request from the first electronic device 104 aassociated with the first user 106 a based on a selection of a seconduser profile of the second user 106 b by the first user 106 a. Theconsent request may correspond to a mutual agreement to perform one ormore activities during the personal interaction between the first user106 a and the second user 106 b. The one or more processors in the CMS110 may be further configured to transmit, via the secure communicationchannel, the consent request to the second electronic device 104 b. Theone or more processors in the CMS 110 may be further configured toreceive, via the secure communication channel, the consent response fromthe second electronic device 104 b based on options selected or dataprovided by the second user 106 b on the consent request generated bythe first user 106 a.

In accordance with an embodiment, for the selection of the second userprofile of the second user 106 b by the first user 106 a, one or moreprocessors in the CMS 110 may be further configured to recommend thesecond user profile of the second user 106 b to the first user 106 a viathe user interface of the application program 112 presented at the firstelectronic device 104 a. The second user profile of the second user 106b may be recommended based on a plurality of options presented by theapplication program 112 at the user interface and selected by the firstuser 106 a based on user preferences of the first user 106 a.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to search the second user profile of thesecond user 106 b from a plurality of user profiles based on one or moresearch terms provided by the first user 106 a at the user interface ofthe application program 112 presented at the first electronic device 104a. Each of the plurality of user profiles includes a name, an age, alocation, a family size, an income, a work indicator, a preference ofliving environment, a browsing history, one or more deep learningfactors derived from pictures or visual descriptions, or any combinationthereof.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to generate rating factors for other usersfrom a plurality of users 106 based on one or more machine learningmodels. The rating factor may comprise a probability of a userinteraction of the first user 106 a with another user from the pluralityof users 106. The one or more processors in the CMS 110 may be furtherconfigured to recommend the second user profile of the second user 106 bbased on ranking of the rating factors for the other users from theplurality of users 106.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to enable the first electronic device 104a and the second electronic device 104 b to exchange a plurality ofmessages prior to the personal interaction between the first user 106 aand the second user 106 b. Each of the plurality of messages maycomprise identification information associated with the first user 106 aand the second user 106 b, and a payload comprising a plurality oftext-based messages, voice-based messages, and video messages.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to determine an event based on analysis ofthe plurality of messages using natural language processing techniques.Corresponding to the event, a first set of tasks may be generated forthe personal interaction between the first user 106 a and the seconduser 106 b.

In accordance with an embodiment, the first set of tasks generatedcorresponding to the event may comprise at least generating a calendarentry for both of the first user 106 a and the second user 106 b forscheduling the personal interaction and booking a meeting venue for thepersonal interaction.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to identify a current set of usercharacteristics based on time sequence-based user interactions and theuttered speech signals of each user during the personal interaction. Thecurrent set of user characteristics may be utilized as a training dataset for the prediction of the new set of user characteristics. The oneor more processors in the CMS 110 may be further configured to predict anew set of user characteristics for the first user 106 a and the seconduser 106 b during the personal interaction based on time sequence-basedpersonal interaction, the training data set, and an artificial neuralnetwork model.

In accordance with an embodiment, the uttered speech signals may beextracted from a conversation between the first user 106 a and thesecond user 106 b during the personal interaction.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to enable the first user 106 a and thesecond user 106 b for a direct exchange of one or more identityconfirmation messages via corresponding user interfaces of theapplication program 112 presented at the first electronic device 104 aand the second electronic device 104 b respectively, during or prior tothe personal interaction between the first user 106 a and the seconduser 106 b.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to override the acceptance on the consentrequest by the immediate dissent one of the first user 106 a or thesecond user 106 b for performing an activity during the personalinteraction.

In accordance with an embodiment, the plurality of criteria for thevalidation of the immediate consent or dissent of the first user 106 aor the second user 106 b may comprise at least informed, freely given,reversible, enthusiastic, and specific agreement between the first user106 a and the second user 106 b to perform one or more activities duringthe personal interaction.

In accordance with an embodiment, the one or more processors in the CMS110 may be further configured to publish, based on a user request, atime sequence-based recording of conversation incurred during thepersonal interaction. The published time sequence-based recording of theconversation incurred during the personal interaction may correspond toone of the second set of tasks.

In accordance with an embodiment, other tasks from the second set oftasks performed based on the validation of the immediate dissent of oneof the first user 106 a or the second user 106 b may comprise dialing anemergency number of current location, dialing a number of an emergencycontact person, or activating an emergency alarm sound.

In accordance with an embodiment, the prediction of the new set of usercharacteristics may be further based on social media data of the firstuser 106 a and the second user 106 b collected from one or more publicinformation databases. The social media data may include a plurality ofmedia shared, content posts, social media contacts having a predefinedsocial media distance between user accounts and information relating tothe social media contacts.

Various embodiments of the disclosure may provide a computer readablemedium, such as the non-transitory second computer readable medium 706,having stored thereon, computer implemented instruction that whenexecuted by the second hardware processor 704 causes the CMS 110 toexecute operations for consent detection and consent validation. Inaccordance with an embodiment, the second hardware processor 704 causesthe CMS 110 to execute operations to receive, during a personalinteraction between the first user 106 a and the second user 106 b,uttered speech signals and sensor data of at least the first user 106 afrom at least the first electronic device 104 a, via a securecommunication channel. The personal interaction may be scheduled basedon a consent response, received from the second electronic device 104 b,corresponding to an acceptance of a consent request received from thefirst electronic device 104 a. The second hardware processor 704 furthercauses the CMS 110 to execute operations to determine a confidence scorebased on an intent of both of the first user 106 a and the second user106 b, current sensor data and a new set of user characteristicspredicted for the first user 106 a and the second user 106 b during thepersonal interaction. The second hardware processor 704 further causesthe CMS 110 to execute operations to detect an immediate consent or animmediate dissent of one of the first user 106 a or the second user 106b at a defined timestamp during the personal interaction based on atleast one of a comparison of the confidence score with a thresholdvalue, one or more explicit or implied keywords from the uttered speechsignals, and an extent of deviated values of the sensor data. The secondhardware processor 704 further causes the CMS 110 to execute operationsto validate the immediate consent or the immediate dissent of one of thefirst user 106 a or the second user 106 b based on a plurality ofcriteria. The second hardware processor 704 further causes the CMS 110to execute operations to perform a second set of tasks based on thevalidation of the immediate dissent of one of the first user 106 a orthe second user 106 b.

The proposed system and method for consent detection and validation maybe highly advantageous. In existing systems, audio or audio-visualrecorders may be used to record the consent of both the parties, beforeor during such in-person meetings, about the permissible extent or setboundaries of physical interactions. However, one or both the partiesmay not agree to the usage of such security systems due to privacyissues, as such security systems may be easily tempered with catering tothe intent, need or convenience of one of the parties. In contrast, theproposed system and method for consent detection and validation, asdescribed herein, may provide a secure communication channel thatfacilitates a secure exchange of messages between participating users.Upon confirmation of the acceptance of the consent request, varioustasks, such as, scheduling a meeting or booking a venue, may beautomatically performed to provide a user-friendly experience.

The proposed system and method may intelligently detect an immediatedissent while performing one or more activities during the personalinteraction and may take immediate action by performing various tasks ifthe situation warrants an attention. The proposed system and method mayfurther smartly validate the immediate consent or dissent based on thebasic criteria for consent evaluation. Further, the completeconversation between the participating users incurred during thepersonal interaction may be recorded in a time sequence-based manner.The proposed system and method may also mark key timestamps within therecording to facilitate quick evaluation of the recording by the reader.At any point in time, the participating users may access the secureread-only record of the conversation occurred during the personalinteraction at a later time, whenever a proof of consent or dissent isneeded to support or refute claims to contrary. Thus, the proposedsystem and method provides a smart, secure, robust, legitimate, anduser-friendly tool for consent detection and validation.

Furthermore, the proposed system and method may enable a victim toreport against an accused about an untoward incident (for example, asexual misconduct occurred during a personal interaction) withconfidence and with legally sound evidence. On the other hand, theproposed system and method may enable an alleged perpetrator to producea sound evidence as a solid defense in the court of law and provehis/her innocence in case a false allegation is levelled by an allegedvictim. Thus, the proposed system and method may provide a valuableevidence to assert the truth of the matter during, for example, civilproceedings, criminal prosecution, or in dispute resolution forums.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (for example, hardware) and any softwareand/or firmware (“code”) which may configure the hardware, be executedby the hardware, and/or otherwise be associated with the hardware. Asused herein, for example, a particular processor and memory may comprisea first “circuit” when executing first one or more lines of code and maycomprise a second “circuit” when executing second one or more lines ofcode. As utilized herein, “and/or” means any one or more of the items inthe list joined by “and/or”. As an example, “x and/or y” means anyelement of the three-element set {(x), (y), (x, y)}. As another example,“x, y, and/or z” means any element of the seven-element set {(x), (y),(z), (x, y), (x, z), (y, z), (x, y, z)}. As utilized herein, the term“exemplary” means serving as a non-limiting example, instance, orillustration. As utilized herein, the terms “e.g.,” and “for example”set off lists of one or more non-limiting examples, instances, orillustrations. As utilized herein, circuitry is “operable” to perform afunction whenever the circuitry comprises the necessary hardware and/orcode (if any is necessary) to perform the function, regardless ofwhether performance of the function is disabled, or not enabled, by someuser-configurable setting.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of embodiments ofthe disclosure. As used herein, the singular forms “a”, “an” and “the”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. It will be further understood that theterms “comprises”, “comprising”, “includes” and/or “including”, whenused herein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

Further, many embodiments are described in terms of sequences of actionsto be performed by, for example, elements of a computing device. It willbe recognized that various actions described herein can be performed byspecific circuits (e.g., ASICs, by program instructions being executedby one or more processors, or by a combination of both. Additionally,these sequences of actions described herein can be considered to beembodied entirely within any non-transitory form of computer readablestorage medium having stored therein a corresponding set of computerinstructions that upon execution would cause an associated processor toperform the functionality described herein. Thus, the various aspects ofthe disclosure may be embodied in a number of different forms, whichhave been contemplated to be within the scope of the claimed subjectmatter. In addition, for each of the embodiments described herein, thecorresponding form of any such embodiments may be described herein as,for example, “logic configured to” perform the described action.

Another embodiment of the disclosure may provide a non-transitorymachine and/or computer readable storage and/or media, having storedthereon, a machine code and/or a computer program having at least onecode section executable by a machine and/or a computer, thereby causingthe machine and/or computer to perform the steps as described herein forconsent detection and validation.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to conduct these methods. Computer program in the presentcontext means any expression, in any language, code or notation, eitherstatically or dynamically defined, of a set of instructions intended tocause a system having an information processing capability to perform aparticular function either directly or after either or both of thefollowing: a) conversion to another language, code or notation; b)reproduction in a different material form.

Further, those of skill in the art will appreciate that the variousillustrative logical blocks, modules, circuits, algorithms, and/or stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, firmware, orcombinations thereof. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentdisclosure.

The methods, sequences and/or algorithms described in connection withthe embodiments disclosed herein may be embodied directly in firmware,hardware, in a software module executed by a processor, or in acombination thereof. A software module may reside in RAM memory, flashmemory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk,physical and/or virtual disk, a removable disk, a CD-ROM, virtualizedsystem, or device such as a virtual servers or container, or any otherform of storage medium known in the art. An exemplary storage medium iscommunicatively coupled to the processor (including logic/code executingin the processor) such that the processor can read information from, andwrite information to, the storage medium. In the alternative, thestorage medium may be integral to the processor.

While the present disclosure has been described with reference tocertain embodiments, it will be noted understood by, for example, thoseskilled in the art that various changes and modifications could be madeand equivalents may be substituted without departing from the scope ofthe present disclosure as defined, for example, in the appended claims.In addition, many modifications may be made to adapt a particularsituation or material to the teachings of the present disclosure withoutdeparting from its scope. The functions, steps and/or actions of themethod claims in accordance with the embodiments of the disclosuredescribed herein need not be performed in any particular order.Furthermore, although elements of the disclosure may be described orclaimed in the singular, the plural is contemplated unless limitation tothe singular is explicitly stated. Therefore, it is intended that thepresent disclosure is not limited to the particular embodimentdisclosed, but that the present disclosure will include all embodimentsfalling within the scope of the appended claims.

What is claimed is:
 1. A system, comprising: a memory for storinginstructions; and a processor configured to execute the instructions,and based on the executed instructions, the processor is furtherconfigured to: receive, during a personal interaction between a firstuser and a second user, uttered speech signals and sensor data of atleast the first user from at least a first electronic device, via asecure communication channel, wherein the personal interaction isscheduled based on a consent response, received from a second electronicdevice, corresponding to an acceptance of a consent request receivedfrom the first electronic device; determine a confidence score based onan intent of both of the first user and the second user, current sensordata and a new set of user characteristics predicted for the first userand the second user during the personal interaction; detect an immediateconsent or an immediate dissent of one of the first user or the seconduser at a defined timestamp during the personal interaction based on atleast one of a comparison of the confidence score with a thresholdvalue, one or more explicit or implied keywords from the uttered speechsignals, and an extent of deviated values of the sensor data; validatethe immediate consent or the immediate dissent of one of the first useror the second user based on a plurality of criteria; and perform asecond set of tasks based on the validation of the immediate dissent ofone of the first user or the second user.
 2. The system according toclaim 1, wherein the processor is configured to: receive detailedinformation, pertaining to a plurality of users, from a plurality ofelectronic devices, via the secure communication channel, wherein thedetailed information is captured at the plurality of electronic devicesthrough a user interface of an application program presented at each ofplurality of electronic devices during registration; generate aplurality of user profiles based on the detailed information, pertainingto the plurality of users, received from the plurality of electronicdevices; and store the plurality of user profiles in a user profiledatabase.
 3. The system according to claim 1, wherein the processor isconfigured to: receive, via the secure communication channel, theconsent request from the first electronic device associated with thefirst user based on a selection of a second user profile of the seconduser by the first user, wherein the consent request corresponds to amutual agreement to perform one or more activities during the personalinteraction between the first and the second user; transmit, via thesecure communication channel, the consent request to the secondelectronic device; and receive, via the secure communication channel,the consent response from the second electronic device based on optionsselected or data provided by the second user on the consent requestgenerated by the first user.
 4. The system according to claim 3,wherein, for the selection of the second user profile of the second userby the first user, the processor is configured to recommend the seconduser profile of the second user to the first user via a user interfaceof an application program presented at the first electronic device, andwherein the second user profile of the second user is recommended basedon a plurality of options presented by the application program at theuser interface and selected by the first user based on user preferencesof the first user.
 5. The system according to claim 3, wherein theprocessor is further configured to search the second user profile of thesecond user from a plurality of user profiles based on one or moresearch terms provided by the first user at a user interface of anapplication program presented at the first electronic device.
 6. Thesystem according to claim 5, wherein each of the plurality of userprofiles includes a name, an age, a location, a family size, an income,a work indicator, a preference of living environment, a browsinghistory, one or more deep learning factors derived from pictures orvisual descriptions, or any combination thereof.
 7. The system accordingto claim 3, wherein the processor is configured to: generate ratingfactors for other users from a plurality of users based on one or moremachine learning models, wherein a rating factor comprises a probabilityof a user interaction of the first user with another user from theplurality of users; and recommend the second user profile of the seconduser based on ranking of the rating factors for the other users from theplurality of users.
 8. The system according to claim 1, wherein theprocessor is further configured to enable the first electronic deviceand the second electronic device to exchange a plurality of messagesprior to the personal interaction between the first user and the seconduser, wherein each of the plurality of messages comprise identificationinformation associated with the first user and the second user, and apayload comprising a plurality of text-based messages, voice-basedmessages, and video messages.
 9. The system according to claim 8,wherein the processor is further configured to: determine an event basedon analysis of the plurality of messages using natural languageprocessing techniques; and generate a first set of tasks for thepersonal interaction between the first user and the second usercorresponding to the event.
 10. The system according to claim 9, whereinthe first set of tasks generated corresponding to the event comprises atleast generating a calendar entry for both of the first user and thesecond user for scheduling the personal interaction and booking ameeting venue for the personal interaction.
 11. The system according toclaim 1, wherein the processor is further configured to: identify acurrent set of user characteristics based on time sequence-based userinteractions and the uttered speech signals of each user during thepersonal interaction, wherein the current set of user characteristics isutilized as a training data set; and predict the new set of usercharacteristics for the first user and the second user during thepersonal interaction based on time sequence-based personal interaction,the training data set, and an artificial neural network model.
 12. Thesystem according to claim 11, wherein the prediction of the new set ofuser characteristics is further based on social media data of the firstuser and the second user collected from one or more public informationdatabases, and wherein the social media data includes a plurality ofmedia shared, content posts, social media contacts having a predefinedsocial media distance between user accounts and information relating tothe social media contacts.
 13. The system according to claim 1, whereinthe uttered speech signals are extracted from a conversation between thefirst user and the second user during the personal interaction.
 14. Thesystem according to claim 1, wherein the processor is further configuredto: enable the first user and the second user for a direct exchange ofone or more identity confirmation messages presented on correspondinguser interfaces of an application program presented at the firstelectronic device and the second electronic device respectively, duringor prior to the personal interaction between the first user and thesecond user.
 15. The system according to claim 1, wherein the processoris further configured to: override the acceptance on the consent requestby the immediate dissent one of the first user or the second user forperforming an activity during the personal interaction.
 16. The systemaccording to claim 1, wherein the plurality of criteria for thevalidation of the immediate consent or dissent of the first user or thesecond user comprises at least informed, freely given, reversible,enthusiastic and specific agreement between the first user and thesecond user to perform one or more activities during the personalinteraction.
 17. The system according to claim 1, wherein the processoris further configured to: publish, based on a user request, a timesequence-based recording of conversation incurred during the personalinteraction, wherein the published time sequence-based recording of theconversation incurred during the personal interaction corresponds to oneof the second set of tasks.
 18. The system according to claim 17,wherein other tasks from the second set of tasks performed based on thevalidation of the immediate dissent of one of the first user or thesecond user comprises dialing an emergency number of current location,dialing a number of an emergency contact person, or activating anemergency alarm sound.
 19. A method, comprising: receiving, by aprocessor, uttered speech signals and sensor data of at least a firstuser from at least a first electronic device during a personalinteraction between the first user and a second user, via a securecommunication channel, wherein the personal interaction is scheduledbased on a consent response, received from a second electronic device,corresponding to an acceptance of a consent request received from thefirst electronic device; determining, by the processor, a confidencescore based on an intent of both of the first user and the second user,current sensor data and a new set of user characteristics for the firstuser and the second user during the personal interaction; detecting, bythe processor, an immediate consent, or an immediate dissent of one ofthe first user or the second user at a defined timestamp during thepersonal interaction based on at least one of a comparison of theconfidence score with a threshold value, one or more explicit or impliedkeywords from the uttered speech signals, and an extent of deviatedvalues of the sensor data; validating, by the processor, the immediateconsent, or the immediate dissent of one of the first user or the seconduser based on a plurality of criteria; and performing, by the processor,a second set of tasks based on the validation of the immediate dissentof one of the first user or the second user.
 20. The method according toclaim 19, further comprising: receiving, by the processor, detailedinformation pertaining to a plurality of users from a plurality ofelectronic devices via a user interface of an application programpresented at each of plurality of electronic devices duringregistration, via the secure communication channel; generating, by theprocessor, a plurality of user profiles based on the detailedinformation, pertaining to the plurality of users, received from theplurality of electronic devices; and storing, by the processor, theplurality of user profiles in a user profile database.
 21. The methodaccording to claim 19, further comprising: receiving, by the processor,the consent request from the first electronic device associated with thefirst user based on a selection of a second user profile of the seconduser by the first user, via the secure communication channel, whereinthe consent request corresponds to a mutual agreement to perform one ormore activities during the personal interaction between the first andthe second user; transmitting, by the processor, the consent request tothe second electronic device, via the secure communication channel; andreceiving, by the processor, the consent response from the secondelectronic device, via the secure communication channel, based onoptions selected or data provided by the second user on the consentrequest generated by the first user.
 22. The method according to claim21, further comprising recommending, by the processor, the second userprofile of the second user to the first user via a user interface of anapplication program presented at the first electronic device, andwherein the second user profile of the second user is recommended basedon a plurality of options presented by the application program at theuser interface and selected by the first user based on user preferencesof the first user.
 23. The method according to claim 21, furthercomprising searching, by the processor, the second user profile of thesecond user from a plurality of user profiles based on one or moresearch terms provided by the first user at a user interface of anapplication program presented at the first electronic device.
 24. Themethod according to claim 21, further comprising: generating, by theprocessor, rating factors for other users from a plurality of usersbased on one or more machine learning models, wherein a rating factorcomprises a probability of a user interaction of the first user withanother user from the plurality of users; and recommending, by theprocessor, the second user profile of the second user based on rankingof the rating factors for the other users from the plurality of users.25. The method according to claim 19, further comprising: determining,by the processor, an event based on analysis of a plurality of messagesusing natural language processing techniques, wherein the plurality ofmessages is exchanged between the first electronic device and the secondelectronic device, via the secured communication channel, prior to thepersonal interaction between the first user and the second user; andgenerating, by the processor, a first set of tasks for the personalinteraction between the first user and the second user corresponding tothe event.
 26. The method according to claim 19, further comprising:identifying, by the processor, a current set of user characteristicsbased on time sequence-based user interactions and the uttered speechsignals of each user during the personal interaction, wherein thecurrent set of user characteristics is utilized as a training data set;and predicting, by the processor, the new set of user characteristicsfor the first user and the second user during the personal interactionbased on time sequence-based personal interaction, the training dataset, and an artificial neural network model.
 27. The method according toclaim 19, further comprising enabling, by the processor, the first userand the second user for a direct exchange of one or more identityconfirmation messages presented on corresponding user interfaces of anapplication program presented at the first electronic device and thesecond electronic device respectively, during or prior to the personalinteraction between the first user and the second user.
 28. The methodaccording to claim 19, further comprising overriding, by the processor,the acceptance on the consent request by the immediate dissent one ofthe first user or the second user for performing an activity during thepersonal interaction.
 29. The method according to claim 19, furthercomprising publishing, by the processor, a time sequence-based recordingof conversation incurred during the personal interaction based on a userrequest, wherein the publishing of the time sequence-based recording ofthe conversation incurred during the personal interaction corresponds toone of the second set of tasks.
 30. A non-transitory computer readablemedium, having stored thereon, computer executable code, which whenexecuted by a processor, cause the processor to execute operations, theoperations comprising: receiving uttered speech signals and sensor dataof at least a first user from at least a first electronic device duringa personal interaction between the first user and a second user, via asecure communication channel, wherein the personal interaction isscheduled based on a consent response, received from a second electronicdevice, corresponding to an acceptance of a consent request receivedfrom the first electronic device; determining a confidence score basedon an intent of both of the first user and the second user, currentsensor data and a new set of user characteristics for the first user andthe second user during the personal interaction; detecting an immediateconsent or an immediate dissent of one of the first user or the seconduser at a defined timestamp during the personal interaction based on atleast one of a comparison of the confidence score with a thresholdvalue, one or more explicit or implied keywords from the uttered speechsignals, and an extent of deviated values of the sensor data; validatingthe immediate consent or the immediate dissent of one of the first useror the second user based on a plurality of criteria; and performing asecond set of tasks based on the validation of the immediate dissent ofone of the first user or the second user.